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Rodrigo Alegría R.Alegria@lse.ac.uk Geography and Environment LSE

ERSA Summer School 2006 Countries, Regions and Multinational Firms: Location Determinants in the EU. Rodrigo Alegría R.Alegria@lse.ac.uk Geography and Environment LSE. General ideas. Motivation:

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Rodrigo Alegría R.Alegria@lse.ac.uk Geography and Environment LSE

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  1. ERSA Summer School 2006Countries, Regions and Multinational Firms: Location Determinants in the EU Rodrigo Alegría R.Alegria@lse.ac.uk Geography and Environment LSE

  2. General ideas • Motivation: - Regional integration produces a decrease in the C-P pattern between countries but an increasing C-P pattern within countries (Puga, 1999) (Combes and Overman 2003). - Regional integration fosters the location of multinational activity within the integrated area (UNCTAD, 2001) (Eurostat, 2004). - Attraction of multinational activity is a policy concern. • Question: - Whether MNEs’ location determinants, and in particular, the sign and strength of agglomeration and dispersion forces change when looking at different levels of geographical aggregation. • Preliminary results: - Conditional logit estimations indicate that agglomeration tendencies are more relevant at regional level while dispersion forces come to dominate at country level.

  3. MNEs’ location decision Export Concentration (1) Core European Firm Core region Concentration (2) MNE Dispersion Peripheral region Dispersion (3)

  4. MNEs Geography (1)

  5. MNEs Geography (2)

  6. Data • Dependent variable: 4,803 location choices in the EU from 1997 to 2005. • Independent variables: -market access (GDP, GDPpc, MP) -labour market (Wages, Unemp.) -agglomeration (ManAgg, ForAgg) -other control (Governance, Tax)

  7. Discrete Choice Model • Profit equation: • Estimation of both conditional and nested logit model (McFadden, 1984). • Could we include spatial structure into conditional logit to relax IIA? • Nested model allows to take into consideration that the probability of choosing a region also depends on the characteristics of the country.

  8. Spatial effects in DCM • Spatial dependence: NLM accounts for unobserved correlation between alternatives within the same nests: 1. Can we also introduce spatial dependence? 2. How to distinguish the spatial autocorrelation that may be present in the unobservable autocorrelation assumed within the nests? 3. How to deal with possible spatial dependence between alternatives belonging to different nests? • Spatial heterogeneity: NLM allows to test for the appropriate nested structured, once defined: 1. Can we interpret this as an intuition of possible spatial heterogeneity?

  9. Preliminary Results

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