ERSA Summer School 2006

Countries, Regions and Multinational Firms: Location Determinants in the EU

Rodrigo AlegríaR.Alegria@lse.ac.uk

Geography and EnvironmentLSE

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.

MNEs’ location decision

Export Concentration (1)

Core European Firm

Core region Concentration (2) MNE Dispersion

Peripheral region Dispersion (3)

MNEs Geography (1)

MNEs Geography (2)Code Region GDP ForAggHU10 Közép-Magyarország 19045.55 15.44HU22 Nyugat-Dunántúl 4477.321 10.89HU21 Közép-Dunántúl 4368.022 9.89HU32 Észak-Alföld 4309.005 7.22HU33 Dél-Alföld 4088.474 3.78 Code Region GDP ForAggHU31 Észak-Magyarország 3542.664 7.44 ES51 Cataluña 101367.2 26.89HU23 Dél-Dunántúl 3087.863 5.22 ES30 Comunidad de Madrid 95354.64 7.00

ES61 Andalucia 74097.95 3.33ES52 Comunidad Valenciana 53295.12 3.33ES21 Pais Vasco 34911.99 4.56ES41 Castilla y León 31173.01 3.56

Code Region GDP ForAgg ES11 Galicia 29559.11 2.33PL12 Mazowieckie 27809.08 9.22 ES70 Canarias 22390.12 1.00PL22 Slaskie 18802.67 8.67 ES42 Castilla-la Mancha 18995.16 2.11PL41 Wielkopolskie 12543.63 7.00 ES24 Aragón 17132.94 2.11PL51 Dolnoslaskie 10745.25 10.89 ES53 Illes Balears 13596.55 1.00PL21 Malopolskie 10130 3.33 ES62 Región de Murcia 13308.78 1.11PL11 Lódzkie 8348.132 8.00 ES12 Principado de Asturias 12253.9 1.89PL63 Pomorskie 7736.494 1.56 ES43 Extremadura 9441.276 1.44PL61 Kujawsko-Pomorskie 6698.564 2.33 ES22 Navarra 9322.163 2.11PL42 Zachodniopomorskie 5978.629 3.00 ES13 Cantabria 6917.402 1.67PL31 Lubelskie 5460.189 1.33 ES23 La Rioja 4140.157 1.11PL32 Podkarpackie 5386.787 2.44 ES63 Ceuta 943.5919 1.00PL62 Warminsko-Mazurskie 3783.105 1.67 ES64 Melilla 829.284 1.00PL33 Swietokrzyskie 3591.916 1.67PL34 Podlaskie 3265.751 2.33PL43 Lubuskie 3191.047 3.78PL52 Opolskie 3146.327 2.11

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)

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.

sticscharactericountry observable

sticscharacteri regional observable

and :

j

ij

ijjijij

W

Z

countryjregioniwhere

WZ

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?

Preliminary ResultsVariable 1 2 3 4

Country GDP +(*) -(*) -(*)GDPpc -(*) -(*) -(*)Market Potential +(*) - -(*)Real Wages -(*) -(**) -(**)Unemployment rate +(*) -(*) -(**)ManAgg +(*) +(*) +(*)ForAgg +(*) +(*) +(*)Tax +(*) -(*) -(*)Governance index +(*) +(*) +(*)

Region GDP +(*) +(*) +(*)GDPpc - + -Market Potential +(**) -(**) +(**)Real Wages +(**) +(***) +(**)Unemployment rate +(**) +(***) +(**)ManAgg +(*) +(*) +(*)ForAgg +(*) +(*) +(*)

Inclusive Value 0.862(*)

Number of observations 1,181,292 120,075 1,181,292 1,181,292

Notes:(i) The dependent variable is location choice.(ii)The symbols (*), (**) and (***) denote significance at the 1%, 5% and 10% levelsrespectively.