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Marshall or Jacobs? New insights from an interaction model

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Abstract

The debate on localization and urbanization economies usually neglects interdependencies between the two types of economies. This paper addresses this problem by employing an interaction model using German data covering four different sectors for the years 1998 to 2008. We find that localization and urbanization economies interact negatively in most of the sectors. Furthermore, we study non-linear effects of specialization and diversification. Taking these into account, the signs indicating localization and urbanization economies change at certain thresholds. Our regression analysis shows evidence of localization economies in manufacturing and basic services. Urbanization economies are generally less prevalent in our data.

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Notes

  1. For recent surveys of the existing theoretical and empirical literature, see Rosenthal and Strange (2004) or Beaudry and Schiffauerova (2009).

  2. One exception is the study of Brunow and Hirte (2009) who intend to study the interaction between agglomeration variables and production factors. Because of strong multicollinearity issues, they refrain from testing such interaction effects.

  3. Non-linear effects were studied in a few papers before (see de Lucio et al. 2002; Illy et al. 2011). Nevertheless, the authors who consider this issue do not interpret their results within the framework of interaction models.

  4. Fahrhauer and Kröll (2012) introduce the concept of diversified-specialization. In contrast to their study, we examine sector-specific effects and not overall effects on economic growth in cities. Furthermore, we introduce a different estimation approach which was not part of the literature yet.

  5. Note that we have no data on sector-specific human capital due to data limitations.

  6. The alternative would be to define diversification as the absence (i.e. the inverse) of specialization.

  7. In a model without interaction terms, the marginal effects are only β and γ. For further details on interaction models, see Brambor et al. (2006).

  8. One would suggest that the inclusion of interaction terms leads to strong multicollinearity problems. Brambor et al. (2006) state that dropping interaction terms would result in omitted variable bias, which is a much more striking problem in an empirical setup. Additionally, we have checked the multicollinearity issue using variance inflation factors and found that it is not a problem for our analysis.

  9. Hoechle (2007) suggests using the Driscoll and Kraay estimator in the presence of spatial dependence. Otherwise, he prefers a cluster-robust estimator.

  10. This module was developed by Schaffer and Stillman (2010).

  11. For example, civil servants and self-employed are not included in these figures.

  12. For example, for deflating nominal GVA of Frankfurt am Main, we use the deflator of the German state Hesse. For Munich, we use the deflator of Bavaria. The state-specific deflator is the ratio of unchained GVA measured in previous year prices and nominal GVA measured in actual prices. This approach is used for official statistics in Germany.

  13. We exclude the agriculture, mining, energy and water supply sectors as well as the public sector. It is hardly imaginable that, e.g., public service sectors gain from localization or urbanization economies. Public service providers cannot move freely between cities, which makes specialization nearly impossible. Furthermore, every city must provide a certain range of public goods. The effects from specialization in the mining sector are due to the initial wealth of natural resources and not to localization economies.

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Acknowledgements

We are grateful to Georg Hirte, Marcel Thum and participants of the Brown Bag Seminar presented by the Faculty of Business and Economics at the Dresden University of Technology. We acknowledge very helpful comments from participants of the ERSA 2012 conference in Bratislava and two anonymous referees.

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Authors and Affiliations

  1. Ifo Institute, Leibniz-Institute for Economic Research at the University of Munich e.V., Branch Dresden, University of Munich, Einsteinstr. 3, 01069, Dresden, Germany

    Jan Kluge & Robert Lehmann

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  1. Jan Kluge
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  2. Robert Lehmann
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Correspondence to Robert Lehmann.

Appendix: Tables 3–6 and Figs. 7 and 8

Appendix: Tables 3–6 and Figs. 7 and 8

Table 3 Descriptive statistics—manufacturing
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Table 4 Descriptive statistics—advanced services
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Table 5 Descriptive statistics—construction
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Table 6 Descriptive statistics—basic services
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Fig. 7
figure 7

Scatter plot for ln(Spec) and ln(Div)

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Fig. 8
figure 8

GVA per employee for the cities in our sample

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Kluge, J., Lehmann, R. Marshall or Jacobs? New insights from an interaction model. Jahrb Reg wiss 33, 107–133 (2013). https://doi.org/10.1007/s10037-013-0076-7

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