boudier bensebaa 2005 economics of transition

© The European Bank for Reconstruction and Development, 2005.Published by Blackwell Publishing, 9600 Garsington Road, Oxford OX4 2DQ, UK and 350 Main Street, Malden, MA 02148, USA.

Economics of TransitionVolume 13 (4) 2005, 605–628

Blackwell Publishing, Ltd.Oxford, UKECOTThe Economics of Transition0967-0750© The European Bank for Reconstruction and Development, 2005.if knownOriginal Article

A

gglomeration

E

conomies

and

L

ocation

C

hoice

B

oudier

-B

ensebaa

Agglomeration economies and location choice

Foreign direct investment in Hungary

1

Fabienne Boudier-Bensebaa

University of Paris XII – Val de Marne, ERUDITE, 61, avenue du Général de Gaulle – 94010 Créteil cedex, France. E-mail: [email protected]

Abstract

Since the beginning of the transition process, Hungary has attracted a significantamount of foreign direct investment (FDI), although this is unevenly distributedamong the twenty Hungarian counties. This paper examines the determinants ofFDI at a regional level in Hungary and more particularly assesses the importanceof agglomeration effects among determinants. A panel model of the location deter-minants of FDI in Hungary is developed and estimated. Empirical testing suggeststhat counties with higher labour availability, greater industrial demand and highermanufacturing density attract more FDI. Surprisingly, higher unit labour costsattract FDI. In addition, inter-industrial agglomeration economies and infrastructureavailability are found to be important.

JEL classifications: C23, F21, F23, O18, R12.Keywords: Agglomeration economies, foreign direct investment, panel estimation,regional location, transition.

1

The author is grateful to both anonymous referees for their perceptive and constructive comments andsuggestions on the first draft of this paper. Thanks also to A.-G. Bonlarron, who provided valuable researchassistance. The author is also indebted to P. Blanchard and J. Lochard for helpful econometrical discussions.

606

B

oudier

-B

ensebaa

1. Introduction

Since the 1990s, most of the world’s foreign direct investment (FDI) is ‘two-wayFDI’, that is between the most developed countries, which are both home andhost countries of FDI. However, FDI in developing and emerging economies stillfollows the 1950s one-way FDI model; they attract FDI coming above all fromdeveloped countries, but they do not act as important home countries. Moreover,FDI targets only some of such developing and emerging countries, one of whichis Hungary. This recent trend towards a polarization of FDI among countries is alsoevident at a regional level. For the location choice, regions within a country can bemore easily substituted for each other than regions belonging to different countries(Mayer and Mucchielli, 1999).

This uneven spatial distribution of foreign economic activities and the tendencyof foreign firms to cluster together led to the question of what determinants pertainto the choice of location by multinational enterprises (MNEs), with Hungary as theexample. Once an MNE has decided to locate a plant in Hungary, what determineswhy is it set up in one particular county rather than another?

According to traditional trade theory, the decision depends on interregionaldifferences in factor and resource endowments. Because countries cannot beconsidered as homogeneous spaces, individual firms have to choose between a varietyof locations and tend to concentrate in favourably endowed regions.

Such clustering of firms, by leading to agglomeration externalities, adds furtherto the attractiveness of the location (Head

et al.

, 1995). Thus, firms tend also tocluster because of the positive externalities generated by proximity. Hence inaddition to the endowment-driven localization theory, explanations of the locationchoice of MNEs can also be drawn from economic geography. In this respect,externalities related to proximity become a major explanation for the locationchoice of MNEs.

According to Marshall (1920), three sources of positive externalities can be iden-tified. Locating near to each other provides firms variously with access to special-ized input suppliers and customers, a shared pooled market for skilled labour, andtechnological spillovers through facilitating information exchange. To these threetraditional sources of positive externalities should be added the many differentforms of localized externalities, namely backward and inward linkages issuingfrom the dynamics of the interaction of firms with other firms, institutions andinfrastructures (Nachum, 2000). This line of reasoning is all the more relevant sincethe organizational structure of MNEs has changed since the end of the ‘golden age’of Western economic growth. The greater volatility of the international businessenvironment has led to a search for more flexible forms of organization (Buckleyand Casson, 2000), and therefore to the end of hierarchical capitalism (Dunning,1995). This in turn has changed the nature of the external linkages of the firms(Nachum, 2000), both in terms of design and location. Firms focus on their corecompetence while increasing outsourcing. In other words, vertical integration has

A

gglomeration

E

conomies

and

L

ocation

C

hoice

607

been discouraged and networks of independent firms have emerged (Harrison,1994, Part III). These firms are often neighbours.

In the light of these theoretical issues and, as raised by Head

et al.

(1995, p. 224),the question is a matter of deciding to what extent the pattern of FDI locationwithin a country ‘

support[s] an agglomeration–externalities theory of industry localiza-tion rather than a theory based on inter-state differences in endowments of naturalresources, labour and infrastructures

’. In this respect, the aim of this paper is to assessthe determinants of location choice by foreign investors in Hungary, with particu-lar emphasis on the existence and magnitude of agglomeration economies.

Both theoretical and empirical work has addressed the process of locationchoice at the international level, but has rarely analysed the sub-national (i.e.,regional) distribution of FDI with a focus on agglomeration effects; even less hasthis been done in relation to Central and Eastern European Countries (CEECs) (seeTable 1 below). Many academic papers have explored the determinants of locationchoice by foreign investors within the USA (Bartik, 1985; Carlton, 1983; Coughlin

et al.

, 1991; Friedman

et al.

, 1992; Head

et al.

, 1998; Head

et al.

, 1994, 1995, 1999;Luger and Shetty, 1985; Nachum, 2000; Woodward, 1992). Other papers have donethe same for large countries other than the USA or unions of countries in relationto foreign investors as a whole or investors originating from a particular country.Among recent studies, some have focused on the regional choices of foreign inves-tors in China (Head and Ries, 1996; Cheng and Kwan, 1999, 2000; He, 2002), whileothers have been concerned with the choices of foreign investors in Europe (Barrelland Pain, 1999; Clegg and Scott-Green, 1998; Devereux and Griffith, 1998; Ferrer,1998; Mayer and Mucchielli, 1998, 1999; Mucchielli and Puech, 2003; Scaperlandaand Balough, 1983). Only a few empirical studies have assessed the locationmotivations of FDI at a more local level. For example, Guimarães

et al.

(2000) haveexamined such motivations for Portugal, and Cantwell and Iammarino (2000) forthe United Kingdom. But among recent studies, by far and away the most compre-hensive at a local level is that by Crozet

et al.

(2003) for France.As far as the CEECs are concerned, there have been few empirical studies of the

location determinants of FDI and of the agglomeration effects among determinants(Kinoshita and Campos, 2003; Lankes and Venables, 1996). To my knowledge, thereis no existing study of this pattern for one particular transition country. Indeed,this type of research faces difficulties at an empirical level. Due to data collectionproblems (data for state, regional and county levels is scarce and not always mutu-ally consistent), the measurement of agglomeration effects in transition economiesmay be particularly problematic. In addition, the period of time over which tran-sition has been underway in CEECs is relatively short. Both these reasons can makeany econometric test problematic.

This paper examines the determinants of FDI at a regional level in Hungary andmore particularly assesses the importance of agglomeration economies amongdeterminants. It is organized as follows. Section 2 examines the regional distributionof FDI in Hungary, Section 3 describes the data and the model, and finally Section 4

608

B

oudier

-B

ensebaa

Table 1. Summary of recent research (since the 1980s) on location of FDI

The main results or variables influencing MNE location decisions are shown in brackets. Statistically significant results are denoted by + or

−

.

Among countriesChen and Chen (1998)

: Taïwan’s FDI in Southeast Asia(Agglomeration economies arising from inter-firms linkages)

Culem (1988)

: FDI among 6 industrialized countries(Unit labour cost (

−

), market growth (+), export flows (+))

Kinoshita and Moody (2001)

: Japanese FDI(Own previous investment in the country and investments by competitors)

Kravis and Lipsey (1982)

: US FDI(Labour cost (no major impact), market size (+), degree of openness (+))

Smith and Florida (1994)

: FDI by Japanese automotive suppliers(Labour quality (+), proximity to Japanese automotive assembly plants (+), population (+), manufacturing density (+), infrastructure (+))

Wheeler and Mody (1992)

: US FDI(Dominant influence of agglomeration economies, limited impact of incentives)

of which CEECsKinoshita and Campos (2003)

: (Dominant influence of institutions and agglomeration economies and then abundance of natural resource and labour cost)

Lankes and Venables (1996)

: (Increasing importance of cost factors suggested by the shift from FDI serving local markets to FDI motivated by serving export markets)

Within the USBartik (1985)

: (Labour cost (

−

), unionization (

−

), manufacturing density (+), infrastructure (+), taxes (

−

))

Carlton (1983)

: (Labour availability (+ or – according to the industry), labour cost (imprecise effect), energy costs (

−

), agglomeration economies (+), pool of technical expertise (+), taxes (no major impact))

Coughlin, Terza and Arromdee (1991)

: (Labour cost (

−

), unemployment (+), unionization (+), per capita income (+), manufacturing density (+), infrastructures, taxes (

−

))

Coughlin and Segev (2000)

: (Unit labour cost (

−

), labour quality (+), demand (+), agglomeration economies (+), taxes (

−

))

Friedman, Gerlowski and Silberman (1992)

: (Labour cost (

−

), unionization (+), market size (+), infrastructures (+), taxes (

−

), incentives (+))

Luger and Shetty (1985)

: (Labour cost (

−

), agglomeration economies (+))

Nachum (2000)

: Services FDI (Labour quality (+), agglomeration economies (+))

A

gglomeration

E

conomies

and

L

ocation

C

hoice

609

FDI originating from JapanHead, Ries and Swenson (1994,

1999)

: (Small overall impact of state promotion efforts because of the proliferation of offsetting programmes)

Head, Ries and Swenson (1995)

: (Industry-level agglomeration economies (+))

Head, Ries and Ruckman (1998)

: Japanese greenfield investments in the US (Agglomeration economies (+))

Woodward (1992)

: (Labour quality (+), unemployment (

−

), unionization (

−

), poverty rate (

−

), agglomeration economies (+))

Within the European UnionBarrell and Pain (1999)

: US FDI(Labour cost (+ or

−

according to the host country), agglomeration economies (+))

Clegg and Scott-Green (1998)

: Japanese FDIs (results are reported only for EC (6))(Market size (+), trade discrimination (

−

))

Devereux and Griffith (1998)

: US FDI(Unit labour cost (not significant), agglomeration effects (+), tax rate in the choice between European locations)

Ferrer (1998)

: French FDI(Labour cost (

−

), unemployment (+), agglomeration economies (+), infrastructure (+), incentives (

−

))

Ford and Strange (1999)

: Japanese FDI(Labour cost (

−

), labour productivity (

−

), English language ability (+), unionization (

−

), per capita income (+), manufacturing density (+), agglomeration economies (+))

Mayer and Mucchielli (1998,

1999)

: Japanese FDI(Labour cost (

−

), unemployment (+), demand (+), agglomeration economies (+), state policies (not significant))

Mucchielli and Puech (2003)

: French FDI(Labour cost (

−

), market size (+), agglomeration economies (+))

Scaperlanda and Balough (1983)

: US FDI(Market size (+), market growth (+), tariff dismantlement (+))

Within countries other than USA and EUCantwell and Iammarino (2000)

: FDI in the

UK

in innovative activities(The position of the region in the location hierarchy)

Cheng and Kwan (1999,

2000)

: FDI in

China

Table 1.

(cont) Summary of recent research (since the 1980s) on location of FDI

610

B

oudier

-B

ensebaa

(Labour cost (

−

), labour quality (not significant), market (+), agglomeration economies (+), infrastructure (+), incentives (+))

Crozet, Mayer and Mucchielli (2003)

: FDI in

France

(Agglomeration effects especially with French firms (+), distance (

−

), incentives (no impact))

Guimarães, Figueiredo, Woodward (2000)

: FDI in

Portugal

(Labour cost (no major impact), population density (not significant), agglomeration economies (especially service agglomeration) (+))

He (2002)

: FDI in

China

(Labour cost (not significant), agglomeration economies (+), infrastructure (+))

Head and Ries (1996): FDI in China(Agglomeration economies (+) magnified by incentives (+))Hill and Munday (1991, 1992): FDI in the UK(Demand (+), financial incentives (+))Woodward and Rolfe (1993): Export-oriented FDI in the Caribbean Basin(Labour cost (−), unionization (not significant), demand (+), monetary variables (inflation rate (−), and exchange rate devaluation (+)), political stability (+), profit repatriation restrictions (−), free trade zones (+), tax incentives (no impact)).

Table 1. (cont) Summary of recent research (since the 1980s) on location of FDI

presents the results of the empirical analysis. Conclusions are set out in the finalsection.

2. Spatial patterns of FDI in Hungary

Since the beginning of the transition process, Hungary has attracted a noteworthyamount of FDI2, mainly targeting the tertiary sector and originating mostly in theEU.3 But FDI is unevenly distributed among the Hungarian regions.

2.1 A major capital city effectTable 2 shows the distribution of inward FDI across Hungarian counties over theperiod 1990–2000. Foreign-owned branch plants are concentrated in Budapest andtherefore in the region of Central Hungary, which accounted for 69 percent of

2 According to the World Investment Report (UNCTAD, 2003), the stock of inward FDI in Hungary in 2002reached 24.4 billion dollars, i.e., 13 percent of the total FDI to CEECs.3 According to the Hungarian Central Statistical Office, the tertiary sector accounted for 51 percent of inwardFDI stock in 2001, and 76 percent of inward FDI stock in 2001 came from the EU.

Agglomeration Economies and Location Choice 611

inward FDI stock attracted by Hungary in 2000. Of the other regions, WesternTransdanubia and Central Transdanubia are the most attractive to FDI. The proximityeffect plays an important role, particularly in the case of Western Transdanubia,which is the only Hungarian region having a common border with the EU (Austria).Conversely, the least attractive Hungarian region is Southern Transdanubia, a pre-dominantly agricultural region that has been completely marginalised since 1995.

Over the ten-year period, Central Hungary has accounted for approximatelytwo-thirds of FDI, a polarization on the Hungarian capital which became morepronounced in 2000. It is possible that data may be skewed towards FDI in Buda-pest because MNEs declare their investments at the headquarters, which are oftenlocated in the capital, in contrast to their production units which may be elsewhere;nonetheless, the data suggest that there is a strong capital effect, in that firms tendto agglomerate in or around the capital city.

2.2 Relative regional attractivenessIn order to take account of the varying size of the regions, the above table onregional distribution of FDI in Hungary was completed using a relative regionalattractiveness index. This was calculated by dividing the regional share of total FDIby the regional share of gross fixed capital formation.4

Because of the disproportionate weight of Budapest, the index was calculatedwithout taking into account Central Hungary. In Table 3, which displays thisindex, only two regions are less attractive for FDI than for investment in general:Northern Great Plain and Southern Transdanubia, both of whose indexes are less

4 A better index could have been defined by dividing the regional share of employed persons in enterpriseswith FDI by the regional share in employment. However, the lack of data on employed persons in enter-prises with FDI impeded this calculation.

Table 2. Regional distribution of inward FDI stock* in Hungary, 1990–2000, percentage

1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000

Central Hungary 67.7 64.1 64.1 68.4 68.9 64.2 62.5 64.5 64.9 66.0 69.2Central Transdanubia 9.3 8.3 7.1 8.1 7.9 6.9 7.6 7.4 6.7 6.8 7.6Western Transdanubia 5.7 9.2 8.5 8.1 8.8 10.3 8.9 9.0 9.3 9.2 7.8Southern Transdanubia 3.9 3.2 3.8 3.6 3.5 3.8 3.2 3.0 3.1 2.0 1.8Northern Hungary 4.8 6.7 6.9 4.3 3.9 5.1 8.7 7.2 7.1 6.7 5.6Northern Great Plain 5.0 4.7 4.1 3.0 2.9 4.5 4.4 4.4 4.3 4.5 3.6Southern Great Plain 3.5 3.8 5.5 4.6 4.1 5.2 4.7 4.6 4.6 4.7 4.3

Source: Hungarian Central Statistical Office and own calculations.Notes: *The regional distribution based on the number of enterprises with FDI displays a similar pattern.

612 Boudier-Bensebaa

than 1. This confirms the earlier observation that Southern Transdanubia was theleast attractive region for FDI (see Section 2.1 above). Central Transdanubia expe-rienced a relative downturn in attracting inward FDI between 1995 and 1998, butthen recovered in 1999.5

5 Central Transdanubia is the one and only region that did not benefit from FDI in the energy sector, asthere is no energy company in this region.

Table 3. Relative regional attractiveness index (without Central Hungary)

1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000

Central Transdanubia 1.601 1.077 1.094 1.441 1.342 0.948 0.947 0.85 0.9 1.05 1.178Western Transdanubia 1.119 1.437 1.017 1.324 1.319 1.34 1.119 1.273 1.43 1.294 1.144Southern Transdanubia 0.841 0.651 0.85 0.786 0.895 0.924 0.862 0.775 0.802 0.489 0.549Northern Hungary 0.885 1.266 1.249 0.913 0.816 0.981 1.359 1.322 1.056 1.013 1.013Northern Great Plain 0.875 0.818 0.698 0.527 0.561 0.72 0.842 0.845 0.752 0.828 0.738Southern Great Plain 0.635 0.651 1.056 0.926 0.873 0.989 0.763 0.867 0.948 1.128 1.149

Source: Hungarian Central Statistical Office and own calculations.


In relation to the size of the regions, there is no great variation in regionalattractiveness for FDI. In fact, Hungary is very clearly split in two along a northwest/southeast axis (see map above). In relative terms, the western and northern Hun-garian regions (Central Hungary, Central Transdanubia, Western Transdanubiaand Northern Hungary) have clearly fared better than those of the south and east(Southern Transdanubia, Northern Great Plain and Southern Great Plain). Thesemarked patterns of geographic concentration suggest the need to go further inassessing the location determinants of FDI in Hungary and the agglomerationeffects among them.

3. Description of the data and the model

3.1. Data and the choice of the panel data methodEmpirical research on FDI and the behaviour of MNEs is constrained by FDIdefinition problems as well as by the notable lack of reliable data, which leads toproblems in the measurement of FDI. The best way to assess FDI location choice isto observe individual firms. Indeed, most of the preceding empirical works haveused firm-based data, and the location determinants of FDI have been analysed asthe result of discrete choice by firms among a number of locations. In such cases,it is possible to model the location determinants of FDI by extensively using theconditional logit method, which is a more appropriate way of studying individualfirms’ decisions in that it allows the introduction of a qualitative endogenousvariable.

However, in the case of Hungary, it is impossible to gather firm-based data.This paper therefore makes use of a dataset from the Hungarian Central StatisticalOffice, which is the only suitable data source on FDI in Hungary for locationanalysis, since this is the only source of data at local level. The Hungarian CentralStatistical Office provides data on inward FDI and macroeconomic aggregates forthe twenty counties and the seven regions in which they are grouped. This confersgreat homogeneity on the variables, since the dependent variable, as well as theindependent ones, can be based on data drawn from the same source.

The lack of availability of firm-based data was compensated for by analysingthe location determinants of FDI in a counties/time double dimension. The use ofFDI stocks as dependent variable prevented the use of the conditional logit method.Following earlier studies such as Barrell and Pain (1999), Ferrer (1998), Hill andMunday (1992), Mathieu (1995), and Driffield and Munday (2000), the locationdeterminants of FDI in Hungary were modelled using panel data methods.

There are a number of reasons for adopting this approach (see Hsiao, 2003, andSevestre, 2002). Panel data methods were first used at the end of the 1980s at acountry level and subsequently at a regional level to emphasize the determinantsof FDI. First, in general terms, panel data are more informative and their great


variability allows several competing hypotheses to be tested. Second, becauseof their double dimension (that is, firms, industries or countries and time dimen-sions), panels are better instruments than common time series or cross section formodelling heterogeneous behaviours (in this case, Hungarian counties). Third, theintroduction of specific effects enables the influence of unobservable characteristicson the dependent variable to be taken into account. It is usual to differentiate fixedeffects from random effects, depending on whether or not the specific effects areassumed to be constant over the individuals.

3.2 Definition of the dependent variableFollowing Devereux and Griffith (1998), this paper investigates the productionundertaken by MNEs, and hence considers FDI stocks. The dependent variable isthe distribution of the FDI stocks in county i on ten-year data (1991–2000). Thelocation alternatives available to MNEs consist of twenty contiguous Hungariancounties. Assuming that the location choices of MNEs are based on information onthe previous year, distribution of FDI stocks across counties (1991–2000) are pairedwith values for independent variables from the previous year (1990–99). Hencethere are cross-sectional time-series on i = 1, . . . , 20 and t = 1, . . . , 10, so that thesample size rises to 200 observations.

The drawback of using these data is that they are overly aggregated, for itdoes not provide information on the form of foreign investment or the regionaldistribution of FDI across home countries and industries.

3.2.1 Location choices according to the form of investmentThe present author agrees with Friedman et al. (1992, p. 404) that investmentdecisions differ according to their form. In particular, mergers and acquisitions(M&A) often correspond to market-seeking FDI. Moreover, in contrast to green-field investments, they pay little regard to specific site location and their agglom-eration effects are therefore ambiguous. First, because of the lack of data, it wasnecessary to lump together all forms of investment. Second, it is worth notingthat until 2000 M&A was the preferred form of FDI for MNEs6 to enter a foreignmarket, particularly in industries with oligopolistic structures, since it is a meansof competing through the redistribution of the market share involved. This behav-iour on the part of MNEs was particularly marked in the CEECs during the firstdecade of the transition. Indeed, because of their systemic particularities, theprivatization process created many acquisition opportunities for foreign firms, par-ticularly in Hungary,7 where privatization was implemented by direct commercialsales.

6 In 1999 the ratio of the value of cross-border M&As to FDI inflows amounted to four-fifths (UNCTAD,2000, p. 113).7 As in Eastern Germany and Estonia.


3.2.2 Location choices according to the investing countriesSince the data available did not permit the various investing countries to be distin-guished, it was therefore not possible to test whether firms tend to follow locationchoices according to their specific national characteristics, as has been proposedby previous research (Head, Ries and Swenson, 1995; Smith and Florida, 1994 forJapanese FDI). FDI in Hungary was thus aggregated, irrespective of the country oforigin. By effectively treating FDI as if it came from only one investing country,FDI stocks can be used as a dependent variable in money terms (Culem, 1988).Hence a Hungarian county is assumed not to be in competition with the countryof origin of the multinational firm making the investment, and therefore to beexclusively sought for itself.

3.2.3 Location choices according to industriesThe main limitation to this research derives from the lack of data on an industry-by-industry basis, because the motivations for investment abroad depend on thecharacteristics of the industry the firm belongs to (Horst, 1972) and, in particular,on its capital or labour intensity. The lack of available data prevented testing theextent to which the location choice of MNEs in Hungary is motivated by a strategy oflow cost production or of access to local markets. Moreover, it also made it unfeasibleto distinguish between inter- and intra-industrial agglomeration economies.

In sum, the available data only allowed cross-county and variation over time tobe used, but without cross-industry and cross-country of origin variations.

3.3 Definition of independent variables and hypothesesThis sub-section discusses the potential determinants of FDI in Hungary. FollowingHead et al. (1994, 1995, 1999) as well as Crozet et al. (2003), four categories of locationdeterminants can be distinguished: labour market conditions, demand conditions,agglomeration economies, and incentives. Table 4 shows explanatory variablesdistinguished according to the available data and provides the definition for eachof them.

3.3.1 Labour market conditionsLabour availability (symbolized by LABAVAI) is measured by the unemploymentrate of each county. A high unemployment rate could be seen as having a deterrenteffect on FDI, in so far as MNEs consider it as a sign of rigidity of the labour market(for example, minimum wage, unemployment contributions, low mobility throughregional migration). However, most empirical studies support the idea that highunemployment favours FDI by providing access to a pool of potential labour(Head et al., 1999), which can also affect factor costs. Unemployment rates acrosscounties can thus be expected to be positively correlated with FDI.

The other labour variable of primary concern for location choices of MNEs islabour cost. Previous empirical research (see Coughlin et al., 1991, p. 677) assumes


Table 4. Definitions and expected impacts of explanatory variables

Name Label Definition Expected impact

Labour market conditionsLabour availability LABAVAI Rate of unemployment in

each county in percentage+

Unit labour cost LABUNCT Labour cost (average annual gross earnings per county in forints) divided by labour productivity (county industrial production in forints divided by manufacturing employment) for each county

?

Demand conditionsPopulation DEMAND1

= POPNumber of inhabitants per county

+

Population share DEMAND2 = POPSHARE

Share of each county in the Hungarian population

+

Population density

DEMAND3 = POPDENS

Number of inhabitants per square mile for each county

+

Manufacturing density

DEMAND4 = MANDENS

County’s manufacturing employment per square mile

+

Industrial demand

DEMAND5 = DEMIND

Manufacturing production minus manufacturing export and plus manufacturing import in millions of forints for each county

+

Agglomeration effectsInter-industrial economies 1

INTER1 Number of enterprises with FDI per county

+

Inter-industrial economies 2

INTER2 Number of enterprises per county

+

Infrastructures availability 1

INFRAS1 = INFROAD

Road mileage in metres per square mile of county land area

+

Infrastructures availability 2

INFRAS2 = INFTEL

Telephone lines per county per 1,000 inhabitants

+


that a high labour cost will deter inward FDI, with the result that MNEs arelikely to relocate labour-intensive activities to low-wage regions. But lower-wage locations are attractive only insofar as this advantage is not offset by lowerproductivity, that is, unit labour cost is itself low. Therefore, following Coughlinand Segev (2000), Devereux and Griffith (1998), Kravis and Lipsey (1982), andWoodward (1992), a proxy of productivity was introduced (county industrialproduction divided by manufacturing employment) in addition to the labourcost variable, in order to define a unit labour cost variable (LABUNCT). If datahad been available, it would have been possible to have taken into account theunit labour cost differential between the investors’ home country and theHungarian county chosen as the production location, which is important sincean overvalued currency in the host country can counteract low wages (Culem,1988).

However, some research indicates that labour cost has no major influence onthe location decisions of MNEs. Moreover, labour cost might be non-significantand/or positively correlated with FDI if the variable captures not only cost effectsbut also skill effects (Devereux and Griffith, 1998; Guimarães et al., 2000; Headet al., 1999; Kravis and Lipsey, 1982). Thus the sign of the regression coefficientsassociated with LABUNCT is uncertain.

3.3.2 Demand conditions: the problem of the boundaries of demandThe main current hypothesis is that market potential is the most importantmotivation underlying FDI at a national level. A large market allows economies oflarge-scale production. Moreover, a growing market offers promising prospectswhich, in accordance with the acceleration principle, stimulate new investment,including FDI (Culem, 1988, pp. 888–889). Some authors also maintain this hypoth-esis for CEECs (Meyer, 1998). The size of the market is usually indicated by GDP,which can be expected to be related positively to FDI.

At a sub-national level, however, do foreign investors serve only the regionwhere they locate? A multinational enterprise may serve a market which differsfrom the borders of the region or of the country the firm invested in. Hungariancounties are relatively small, and firms can clearly target consumers well beyondthe county they produce in. Thus the county per capita income is not a very appro-priate proxy for market demand. But ‘theory provides no guidance as to a potentiallysuperior proxy’ (Coughlin et al., 1991, p. 677, note 6).

Crozet et al. (2003, p. 6) use a variable derived from Harris’s market potential(1954). Coughlin et al. (1991) take the GDP of the region the firm locates in, andadd that of the neighbouring regions divided by the distance to the homeregion. Manufacturing density might be another proxy for market demand, asis proposed by Coughlin et al. (1991). Foreign firms tend to locate in countieshaving high levels of manufacturing activity, by virtue of proximity to existingmanufacturers and the potential market these represent (Coughlin et al., 1991;Head et al., 1995; Smith and Florida, 1994; Wheeler and Mody, 1992; Woodward,


1992).8 Similarly, Mayer and Mucchielli (1999, p. 19) construct an industrialdemand variable, which can capture ‘input-output linkages’ (the vertical linkagesbetween downstream and upstream firms). Thus defined, market demand couldalso be seen as a proxy for agglomeration economies (Coughlin et al., 1991), reflectingadvantages gained from spillovers (Devereux and Griffith, 1998, p. 351).

Because it would appear that constructing a satisfactory measure of marketdemand is rather difficult, it was decided to run the regression with three alternativeproxies. Since GDP is not available for Hungarian counties before 1994, it was notpossible to follow Crozet et al. (2003) except by dramatically reducing the numberof observations. Instead of GDP, we took the population (POP), the populationshare (POPSHARE), and the population density (POPDENS) as a first group ofproxies for market demand. Then, following Mayer and Mucchielli (1999), we usedthe industrial demand for each county (DEMIND) (manufacturing productionminus manufacturing export plus manufacturing import) as an alternative variable;but due to the lack of data for some counties for the year 1990, this reduced thesample to 164 observations. Finally, following Coughlin et al. (1991), we introducedmanufacturing density per county (MANDENS) (i.e., the county’s manufacturingemployment per square mile) as proxy both for market demand and for agglom-eration economies. Regression coefficients associated with these alternative variablesare expected to be positive.

3.3.3 Agglomeration effectsThe aim of this paper is to analyse the determinants of FDI at a regional level andin particular to assess the importance of agglomeration effects among determinants.Numerous sources of agglomeration can be identified and empirical studies showtheir importance (see Table 1).

Firms benefit from geographical proximity because of agglomeration econo-mies, which can be intra-industrial (localization economies) or inter-industrial(urbanization economies). In the case of localization economies, firms locate in anarea where their sector of activity is strongly developed (Arthur, 1994; Marshall,1920; Venables, 1994), inducing a specialization of the region in the industryforeign firms had previously invested in. External economies are internalized atsectoral level and are linked to (i) economies as a result of a stronger division oflabour between firms; (ii) the creation of specialized labour market service facilitiespeculiar to the sector; and (iii) the opportunity to benefit from specific non-tradableintermediary goods, and from more diversified and less expensive inputs at theroot of pecuniary externalities.

In accordance with the economies of urbanization, firms belonging to differentindustries gather in the same place. Urbanization economies are external to thefirm and to the industry, but internal to the urban region. On the supply side, they

8 McConnell (1980) found that the preference of foreign investors in the US for the traditional manufacturingheartland was beginning to weaken. We did not find any subsequent research confirming this conclusion.


are linked to (i) the diversity and the quality of infrastructure and service facilities;(ii) incentives; (iii) access to a qualified and large labour force; and (iv) bettercirculation of information, thereby favouring contacts (Glaeser et al., 1992) anddisseminating information, particularly in relation to innovations, which initiallyspread around their point of origin (Amin, 1993; Jaffe et al., 1993). On the demandside, they are related to (i) market openings and (ii) ‘flexibility insurance’ (Veltz,1993) or risk minimization.

In this research, because of the lack of disaggregated data across industries,it was not possible to distinguish precisely between these two different types ofagglomeration economies. We identified two alternative agglomeration measures,following Head et al. (1995), Mayer and Mucchielli (1998, 1999), and Crozet et al.(2003), but without taking into account the industry dimension. The first variable(INTER1) is the number of foreign plants operating in each county one year priorto the investment; the second (INTER2) is the cumulated addition of firms (bothHungarian and foreign) located in each county one year before the investment overthe 1990–2000 period. This latter variable, often used as measure of agglomeration(He, 2002; Head et al., 1999; Woodward, 1992), can be considered as a proxy formanufacturing density, which can also be measured by manufacturing employmentper square mile (Coughlin et al., 1991; Guimarães, 2000), as defined above(see section 3.2). A positive coefficient is expected for these three measures ofagglomeration.

These variables do not reflect the process taking place within firms belongingto a particular industry, but only relate to the effects emerging from the clusteringof firms in a particular location. They might indicate that the location process ispath-dependent, since the current attractiveness of a region is strongly linked to itspast attractiveness, to the initial conditions (Arthur, 1989; Rauch, 1993) and notonly to its intrinsic advantages. ‘History, in the form of sunk costs resulting from theoperation of many firms at a site, creates a first-mover disadvantage’ (Rauch, 1993, p. 843).This points to the importance of the learning effect. A foreign firm’s location choiceis influenced by existing economic activity, that is, the prior presence of other firms(Head and Ries, 1996; Cantwell and Iammarino, 2000), and possibly by its ownprevious experience of the market (Kinoshita and Mody, 2001) or by the experienceof firms from its own country of origin (Smith and Florida, 1994). Previous decisionstaken by firms can be perceived as an attractiveness signal for a site. The existingdistribution of both domestic (Banerjee, 1992; DeCoster and Strange, 1993) andforeign activities (Head and Ries, 1996) are channels of information concerning thebest locations. In other words, the attractiveness of a location is a positive functionof the firms that have already established themselves there, be they local or foreign.Moreover, locating close to foreign firms brings benefits from knowledge spilloverand the direct experiences of previous investors, as well as minimizing informationcosts (He, 2002). These spatially bounded increasing returns lead to a strongself-reinforcing effect of FDI (Arthur, 1988, 1994; Krugman, 1991; Cheng andKwan, 2000).


Advantages deriving from infrastructure availability are also sources ofagglomeration. Developed infrastructure facilitates transportation and communica-tion, and therefore benefits access to input markets, the supply of regions at adistance, and the diffusion of information. By determining a location’s accessibility,infrastructure partly determines its competitiveness. Moreover, developed infra-structure leads to lower transport costs and therefore to the spatial agglomeration ofeconomic activities at some poles to the detriment of ‘intermediate locations’ (Jayetet al., 1996, p. 150). It can even harm the development of less industrialized regions.

To detect such an effect, I had data on the number of telephone lines (INFTEL)and the road mileage for each county. Hence we were able to follow Coughlinet al. (1991) by using the road mileage per square mile of county land area(INFROAD) and Head and Ries (1996) by using the number of telephone lines per1,000 residents. The coefficient is expected to be positively correlated to FDI.

3.3.4 Taxes and incentives: An unsettled questionThe effect of policies designed to enhance attractiveness (for example, tax exemp-tion, service facilities, or privatization policy) is very difficult to ascertain and is amuch debated issue (Head et al., 1994). The question here is whether or not fiscalpolicies have an effect in attracting foreign firms, whether subsidies and incentivescan offset and overcome the weakness or lack of comparative advantage of aparticular location, by attracting not only initial but subsequent investment,thereby resulting in a cumulative effect. There is evidence both for and against theproposition. (Luger and Shetty, 1985).

According to Carlton (1983), Crozet et al. (2003), Ferrer (1998), and Wheeler andMody (1992, p. 57) incentives have limited impact on the inter-regional location choiceof foreign firms; and even when there is a statistically significant positive effect, itsmagnitude is low in comparison with other determinants. Coughlin et al. (1991, 2000)have found that taxes have a deterrent effect on business location decisions.

Until 1992, the Hungarian Government offered a number of fiscal incentivesaimed at attracting FDI. Since then, such incentives have been considerablyreduced, and today they are only allowed when investments occur in priorityregions, entrepreneurial belts, or underdeveloped areas. Local authorities alsogrant tax reductions in proportion to the size of the investment and its contributionto local employment. However, each of the two measures identified – the numberand the area of the industrial parks per county – were available only for the year1997, which was insufficient for dynamic analysis. Further, it would have beenvery interesting to have tested the impact of the Hungarian government’s privati-zation policy, insofar as this considerably favoured direct sales of public firmsto foreign strategic investors. Unfortunately, the Hungarian Privatisation Agencyrefuses to provide any detailed information. It was therefore not possible to obtaineither the number or the value of sales to foreign firms by county. As a result, wewere unable to define a variable to evaluate the importance of incentives as adeterminant of FDI. The correlation of such a variable with the other variables can


be expected to be low, and its omission should not have too serious an effect onthe estimates of the other regression coefficients.

Finally, year dummies were introduced to control for time variation arisingfrom changes in the external economic environment common to all counties. Thisfactor is particularly important in that Hungary, like other Central and EasternEuropean countries, experienced systemic shocks.

4. Empirical results

The basic estimated equation is the following:

Yi,t = a + bLABAVAIi,t−1 + cLABUNCTi,t−1 + dDEMANDi,t−1 + eINTERi,t−1 + fINFRASi,t−1 + fi + DUMt + uit

with fi = individual effects, DUMt = year dummies, and uit = error term.The Hausman specification test led to the rejection of the random-effect model

in favour of a fixed-effect model.9 Having taken into account several potentialexplanatory variables, the results of five regressions are presented in Table 5.In the first, variant demand is measured by the industrial demand (DEMIND) andagglomeration effects by the number of enterprises with FDI (INTER1) and theroad mileage per square mile of land area (INFROAD). Alternative specificationswere then tested in variants 2 to 4. In Equation 2 the manufacturing density insteadof DEMIND is taken as proxy for demand; in Equation 3 the number of telephonelines per 1,000 inhabitants (INFTEL) instead of INFROAD is used as proxy forinfrastructure; and in Equation 4 inter-industrial economies are measured by thenumber of enterprises instead of INTER1. Finally, the full specification (variant 5)adds manufacturing density as an agglomeration variable to variant 1.

4.1 Labour market conditionsLabour availability has a positive impact on location choice of MNEs in Hungary.The coefficient associated with LABAVAI is highly significant and positive asexpected. High unemployment rates attract MNEs, which seek counties having aplentiful workforce. With regard to the unit labour cost variable (LABUNCT), itscoefficient in variants 1 and 3 is also significant and positive. As already pointed out,the variable may express not only labour cost effects but also skill effects. Moreover,the concentration of firms on poles may induce them to pay more for workersin dense labour markets (Wheaton and Lewis, 2002). The use of manufacturingdensity as a proxy for demand (variant 2) reduces the coefficient of LABUNCT and

9 Hausman statistic: Chi2(14) = 76.27, Prob > Chi2 = 0.


its significance. Manufacturing density may express some of the effects of thelabour cost variable. The use of INTER2 as a proxy for inter-industrial agglomera-tion economies (variant 4) makes the labour cost variable non-significant. However,the chosen labour cost variable is overly aggregated, since the lack of data made itimpossible to distinguish between labour- and capital-intensive activities.

4.1.1 North-western/south-eastern divisionNevertheless, the results of the statistical study (see Section 2 above) indicated thatHungary can be divided into two parts: the north-western regions and the south-eastern regions. Moreover, according to the inventory across industries, homecountries and forms of FDI above 1 million Forints (Bonlarron and Boudier-Bensebaa,2002), it seems that FDI in the northern and western Hungarian regions is morecapital intensive, while that in the southern and eastern regions is more labourintensive (food industry and clothing manufacture, for example). This suggeststhat the regional determinants of FDI are not the same in those two areas: MNEsmight enter north-western regions for market-seeking reasons and south-easternregions for efficiency-seeking reasons. A dummy variable was therefore introduced

Table 5. Alternative specification estimation results

Dependent variable = FDI stocks

Variant No. 1 2 3 4 5Labour market conditionsLABAVAI 14.468*** 10.781*** 16.949*** 6.027*** 10.150**LABUNCT 246.452** 155.684 291.605** −68.874 209.257*

Demand conditionsDEMIND 0.221** 0.298*** 0.189*** 0.139*MANDENS 1.820***

Agglomeration effectsINTER1 0.120*** 0.140*** 0.135*** 0.146***INTER2 0.033***MANDENS 1.752***INFROAD 3.589*** 4.474*** 0.781 4.562***INFTEL −0.134F statistic *** *** *** *** ***R-Squared (within) 0.8243 0.8359 0.8149 0.9481 0.8478Number of observations 184 184 200 184 184

Notes: *, **, *** denote significance at the 10%, 5% and 1% levels.The year dummies have not been reported.


to take account of this division.10 DIVSE is equal to one for counties located in thesouthern or eastern parts of Hungary and to zero for all other counties. By havingthis dummy interact with labour cost or demand variables, it may be possible toassess more precisely the location choice of MNEs in Hungary. Indeed, it appearsthat unit labour cost has a negative effect on FDI in southern and eastern Hungariancounties, whereas the coefficient associated with this variable remains positiveand significant when all the counties are taken into account (see Table 6). Thisfinding supports the hypothesis that MNEs are looking for cheap labour in thesouthern and eastern counties. But since the interaction of the dummy with thedemand variables does not display any relevant results, the presumption that MNEstrategy might be more market-seeking in northern and western counties than insouthern and eastern ones cannot be confirmed.

4.2 Demand conditionsThese results show that MNEs are sensitive to demand conditions. Industrial demandand manufacturing density are positive and significant determinants of MNElocation decisions in Hungary. But results which are not reported reveal that usingvariables defined in terms of population (population, population share or populationdensity) to measure demand leads to significant but negative coefficients. Moreover,their inclusion affects the significance, and even the sign, of the labour conditionsvariables. These negative and statistically significant coefficients are difficult tointerpret. They might be due to the fact that Hungary, like other Central and EasternEuropean countries, has experienced a decrease in population since the late 1980s.

4.3 Agglomeration effectsEquations 1, 2 and 5 show that inter-industrial economies positively influenceMNE location decisions in Hungary. The coefficients of INTER1, MANDENS and

10 Thanks are due to an anonymous referee for suggesting the introduction of this dummy variable.

Table 6. Results of the introduction of an interactive division variable

LABUNCT 1405.828***LABUNCTDIVSE −1049.637***F Statistic ***R-Squared (within) 0.39Number of observations 200

Notes: *, **, *** denote significance at the 10%, 5% and 1% levels.The year dummies have not been reported.


INFROAD are significant and positively linked to FDI. MNEs tend to locate whereother firms have already settled (as measured by MANDENS) and where thereis significant foreign business activity within a particular county (as measured byINTER1). Previous location of firms in the Hungarian counties is a source of tech-nological spillovers for MNEs. The infrastructure level also appears to be a regionaldeterminant of FDI in Hungary. MNEs prefer locations where infrastructure isavailable and developed. However, in alternative specification 4 the inclusion ofINTER2 makes the labour cost and the infrastructure variables non-significant. Invariant 3, the coefficient associated with INFTEL is also non-significant. INFTELmay be a poor proxy for infrastructure, since the renovation of the telephonenetwork took place later than that of the road network. Moreover, the variabledoes not take account of the rapid expansion of mobile phones in Hungary whichcompensates for the underdevelopment of the fixed line network.

5. Concluding remarks and prospects

This research provides an empirical approach to the regional determinants of FDIin CEECs. It may be considered as innovative in as much as this kind of studyhas never, to my knowledge, been carried out for these countries due to the lackof firm-based data and the consequent difficulty of measuring and assessing thedeterminants of FDI.

The results indicate that labour availability, demand conditions and agglomer-ation economies all have a significant and positive influence on the inward FDIattracted by Hungarian counties. Surprisingly, unit labour costs are positivelyassociated with FDI. However, when the geographical division of Hungary is takeninto account, the coefficient of the labour cost variable becomes negative for themore labour-intensive southern and eastern counties. The biggest problem faced indefining the location determinants is how to define a demand variable. First, it isdifficult to define the geographical extent of demand. Hungarian demand does notend at Hungary’s borders but, especially since its integration into the EU, extendsto neighbouring countries. Second, traditional location determinants, among themdemand, overlap with agglomeration economies, thereby making it more difficultto interpret the findings.

Finally, the scope of the current research suffers from a lack of sectoral studyof localization factors, which take into account the differing conditions of compe-tition across sectors. Ideally such research would aim to analyse the localizationfactors of FDI across home countries and sectors. But the lack of available dataforced this study to refer to the aggregated figures for all industries within thecounties. This limitation prevented us from testing the extent to which the locationchoice of MNEs in Hungary is motivated by a strategy of low cost production withaccess to adjacent EU markets or to CEEC markets, and from establishing whetherfirms tend to make location choices on a specific national basis.


Nevertheless, this research is an initial exploration of a topic that is of increasingimportance given that eight of the Central and Eastern European countries haverecently become members of the EU and hence of a single market in which nationalboundaries matter less and less while the importance of regional factors is on theincrease.

References

Amin, A. (1993). ‘The globalization of the economy. An erosion of regional networks?’ inGrabher, G. (ed.), The Embedded Firm: On the Socioeconomics of Industrial Networks, London,New York: Routledge.

Arthur, W. B. (1988). ‘Self-reinforcing mechanisms in economics’, in Anderson, P., Arrow, K.and Pines, D. (eds.), The Economy as an Evolving Complex System, Redwood City:Addison-Wesley Publishing Company.

Arthur, W. B. (1989). ‘Competing technologies, increasing returns and lock-in by historicalevents’, Economic Journal, 99(384), pp. 116–31.

Arthur, W. B. (1994). Increasing Returns and Path Dependence in the Economy, Ann Arbor:University of Michigan Press.

Banerjee, A. (1992). ‘A simple model of herd behaviour’, Quarterly Journal of Economics,107(3), pp. 797–817.

Barrell, R. and Pain, N. (1999). ‘Domestic institutions, agglomerations and foreign directinvestment in Europe’, European Economic Review, 43(4-6), pp. 925–34.

Bartik, T. J. (1985). ‘Business location decisions in the United States: Estimates of the effectsof unionization, taxes and other characteristics of states’, Journal of Business and LocationStatistics, 3(1), pp. 14–22.

Bonlarron, A.-G. and Boudier-Bensebaa, F. (2002). ‘Le capital étranger en Hongrie. Importancedu facteur régional’, Le Courrier des Pays de l’Est, (1022), pp. 46–60.

Buckley, P. J. and Casson, M. C. (2000). ‘Models of the multinational enterprise’, inBuckley, P. J. (ed.), Multinational Firms, Cooperation and Competition in the World Economy,London: Macmillan, pp. 9–43.

Cantwell, J. A. and Iammarino, S. (2000). ‘Multinational corporation and the location oftechnological innovation in the UK regions’, Regional Studies, 34(4), pp. 317–32.

Carlton, D. (1983). ‘The location and employment choices of new firms: An econometricmodel with discrete and continuous endogenous variables’, Review of Economics andStatistics, 65(3), pp. 440–49.

Chen, H. and Chen, T.-J. (1998). ‘Network linkages and location choice in foreign directinvestment’, Journal of International Business Studies, 29(3), pp. 445–68.

Cheng, L. K. and Kwan, Y. K. (1999). ‘FDI stock and its determinants’, in Wu, Y. (ed.), ForeignDirect Investment and Economic Growth in China, Cheltenham: Edward Elgar, pp. 42–56.

Cheng, L. K. and Kwan, Y. K. (2000). ‘What are the determinants of the location of foreigndirect investment? The Chinese experience’, Journal of International Economics, 51(2),pp. 379–400.

Clegg, J. and Scott-Green, S. (1998). ‘The determinant of Japanese foreign direct investmentflows to the European Community, 1963–1990’, in Rugman, A. M. and Mucchielli, J.-L.(eds.), Multinational Location Strategy, Greenwich and London: JAI Press Inc., pp. 29–49.


Coughlin, C. C. and Segev, E. (2000). ‘Location determinants of new foreign-owned manu-facturing plants’, Journal of Regional Science, 40(2), pp. 323–51.

Coughlin, C. C., Terza, J. V. and Arromdee, V. (1991). ‘State characteristics and the locationof foreign direct investment within the United States’, Review of Economics and Statistics,73(4), pp. 675–83.

Crozet, M., Mayer, T. and Mucchielli, J.-L. (2003). ‘How do firms agglomerate? A study ofFDI in France’, CEPR Discussion Papers, International Trade No. 3873, London: Centre forEconomic Policy Research.

Culem, C. G. (1988). ‘The locational determinants of direct investments among industrializedcountries’, European Economic Review, 32(4), pp. 885–904.

DeCoster, G. and Strange, W. (1993). ‘Spurious agglomeration’, Journal of Urban Economics,33(3), pp. 273–304.

Devereux, M. and Griffith, R. (1998). ‘Taxes and the location of production: Evidence froma panel of US multinationals’, Journal of Public Economics, 68(3), pp. 335–67.

Driffield, N. and Munday, M. (2000). ‘Industrial performance, agglomeration, and foreignmanufacturing investment in the UK’, Journal of International Business Studies, 31(1),pp. 21–37.

Dunning, J. H. (1995). ‘Reappraising the eclectic paradigm in an age of alliance capitalism’,Journal of International Business Studies, 26(3), pp. 461–91.

Ferrer, C. (1998). ‘Pattern and determinants of location decisions by French multinationalsin European regions’, in Rugman, A. L. and Mucchielli, J.-L. (eds.), Multinational LocationStrategy, Greenwich and London: JAI Press Inc., pp. 117–38.

Ford, S. and Strange, R. (1999). ‘Where do Japanese manufacturing firms invest withinEurope, and why?’ Transnational Corporation, 8(1), pp. 117–42.

Friedman, J., Gerlowski, D. A. and Silberman, J. (1992). ‘What attracts foreign multinationalcorporations? Evidence from branch plant location in the United States’, Journal ofRegional Science, 32(4), pp. 403–18.

Glaeser, E., Kallal, H. D., Scheinkman, J. A. and Shleifer, A. (1992). ‘Growth in cities’, Journalof Political Economy, 100(6), pp. 1126–52.

Guimarães, P., Figueiredo, O. and Woodward, D. (2000). ‘Agglomeration and thelocation of foreign direct investment in Portugal’, Journal of Urban Economics, 47(1),pp. 115–35.

Hansen, E. (1987). ‘Industrial location choice in São Paulo, Brazil: A nested logit model’,Regional Science and Urban Economics, 17(1), pp. 89–108.

Harris, C. (1954). ‘The market as a factor in the localization of industry in the United States’,Annals of the Association of American Geographers, 64, pp. 315–48.

Harrison, B. (1994). Lean and Mean: The Changing Landscape of Corporate Power in the Age ofFlexibility (Part III: The emerging system of globally networked production, pp. 123–216),London and New York: The Guildford Press.

He, C. (2002). ‘Information costs, agglomeration economies and the location of foreign directinvestment in China’, Regional Studies, 36(9), pp. 1029–36.

Head, K. and Ries, J. (1996). ‘Inter-city competition for foreign investment: Static anddynamic effects of China’s incentives areas’, Journal of Urban Economics, 40(1), pp. 38–60.

Head, K., Ries, J. and Ruckman, K. (1998). ‘Industry agglomeration and the location offoreign affiliates’, in Rugman A. M. and Mucchielli J.-L. (eds.), Multinational LocationStrategy, Greenwich and London: JAI Press Inc., pp. 53–85.


Head, K., Ries, J. and Swenson, D. (1994). ‘The attraction of foreign manufacturing invest-ments: Investment promotion and agglomeration economies’, NBER Working Paper No.4878, Cambridge, MA: National Bureau of Economic Research.

Head, K., Ries, J. and Swenson, D. (1995). ‘Agglomeration benefits and location choice:Evidence from Japanese investment in the United States’, Journal of International Econom-ics, 38(3/4), pp. 223–47.

Head, K., Ries, J. and Swenson, D. (1999). ‘Attracting foreign manufacturing: Investmentpromotion and agglomeration’, Regional Science and Urban Economics, 29(2), pp. 197–218.

Hill, S. and Munday, M. (1991). ‘The determinants of inward investment: A Welsh analysis’,Applied Economics, 23(11), pp. 1761–69.

Hill, S. and Munday, M. (1992). ‘The UK regional distribution of foreign direct investment:Analysis and determinants’, Regional Studies, 26(6), pp. 535–44.

Hsiao, C. (2003). Analysis of Panel Data, Cambridge: Cambridge University Press.Horst, T. (1972). ‘Firm and industry determinants of the decision to invest abroad: An

empirical study’, Review of Economics and Statistics, 54, pp. 258–66.Jaffe, A. B., Trajtenberg, M. and Henderson, R. (1993). ‘Geographic localization of knowledge

spillovers as evidence by patent citations’, Quarterly Journal of Economics, 108(3), pp. 577–98.Jayet, H., Puig, J.-P. and Thisse, J.-F. (1996). ‘Enjeux économiques de l’organisation du

territoire’, Revue d’Economie Politique, 106(1), pp. 128–58.Kinoshita, Y. and Mody, A. (2001). ‘Private information for foreign investment in emerging

economies’, Canadian Journal of Economics, 34(2), pp. 448–64.Kinoshita, Y. and Campos, N. F. (2003). ‘Why does FDI go where it goes? New evidence

from the transition economies’, International Trade and Transition Economics DiscussionPaper Series No. 3984, London: Centre for Economic Policy Research.

Kravis, I. B. and Lipsey, R. E. (1982). ‘The location of overseas production and productionfor export by US multinational firms’, Journal of International Economics, 12(3/4), pp. 201–23.

Krugman, P. (1991). Geography and Trade, Cambridge, MA: MIT Press.Lankes, H. P. and Venables, A. J. (1996). ‘Foreign direct investment in economic transition:

The changing pattern of investments’, Economics of Transition, 4(2), pp. 331–47.Luger, M. I. and Shetty, S. (1985). ‘Determinants of foreign plant start-ups in the United

States: Lessons for policymakers in the Southeast’, Vanderbilt Journal of Transnational Law,28, pp. 223–45.

McConnell, J. E. (1980). ‘Foreign direct investment in the United States’, Annals of theAssociation of American Geographers, 70(2), pp. 259–70.

Marshall, A. (1920). Principles of Economics, London: Macmillan.Mathieu, C. (1995). ‘Panel data, multinational enterprises and direct investment’, in Mátyás, L.

and Sevestre, P. (eds.), The Econometrics of Panel Data, Handbook of Theory and Applications,Dordrecht: Kluwer Academic Publishers, pp. 823–44.

Mayer, T. and Mucchielli, J.-L. (1998). ‘Agglomeration effects, state policies, and competitionin the choice of Japanese FDI in Europe’, in Rugman, A. L. and Mucchielli, J.-L. (eds.),Multinational Location Strategy, Greenwich and London: JAI Press Inc., pp. 87–116.

Mayer, T. and Mucchielli, J.-L. (1999). ‘La localisation à l’étranger des entreprises multina-tionales : Une approche géographique hiérarchisée appliquée aux entreprises japonaisesen Europe’, Economie et Statistique, (326–327), pp. 159–67.

Meyer, K. (1998). Direct Investment in Economies in Transition, Cheltenham: Edward Elgar.


Mucchielli, J.-L. and Puech, F. (2003). ‘Internationalisation et localisation des firmes multi-nationales : L’exemple des entreprises françaises en Europe’, Economie et Statistique,(363-364-365), pp. 129–44.

Nachum, L. (2000). ‘Economic geography and the location of TNCs: Financial and profes-sional service FDI to the USA’, Journal of International Business Studies, 31(3), pp. 367–85.

Newman, R. J. and Sullivan, D. H. (1988). ‘Econometric analysis of business tax impacts onindustrial location: What do we know, and how do we know it?’ Journal of UrbanEconomics, 23, pp. 215–34.

Rauch, J. E. (1993). ‘Does history matter only when it matters little? The case of city industrylocation’, Quarterly Journal of Economics, 108(3), pp. 843–67.

Scaperlanda, A. and Balough, R. S. (1983). ‘Determinants of US investment in the E.E.C.’,European Economic Review, 21(3), pp. 381–90.

Sevestre, P. (2002). Econométrie des Données de Panel, Paris: Dunod.Smith, D. F. and Florida, R. (1994). ‘Agglomeration and industrial location: An econometric

analysis of Japanese-affiliated manufacturing establishments in automotive-relatedindustries’, Journal of Urban Economics, 36(1), pp. 23–41.

UNCTAD (2000). World Investment Report. Cross-border Mergers and Acquisitions and Development,New York and Geneva: United Nations.

UNCTAD (2003). World Investment Report. FDI Policies for Development: National and Interna-tional Perspectives, New York and Geneva: United Nations.

Veltz, P. (1993). ‘D’une géographie des coûts à une géographie de l’organisation : Quelquesthèses sur l’évolution des rapports entreprises/territoires’, Revue Economique, 44(4),pp. 671–84.

Venables, A. J. (1994). ‘Economic integration and industrial agglomeration’, Economic andSocial Review, 26(1), pp. 1–17.

Wheaton, W. C. and Lewis, M. J. (2002). ‘Urban wages and labor market agglomeration’,Journal of Urban Economics, 51(3), 542–62.

Wheeler, D. and Mody, A. (1992). ‘International investment location decisions: The case ofUS firms’, Journal of International Economics, 33(1–2), pp. 57–76.

Woodward, D. P., (1992). ‘Locational determinants of Japanese manufacturing start-ups inthe United States’, Southern Economic Journal, 58(3), pp. 690–708.

Woodward, D. P. and Rolfe, R. J. (1993). ‘The location of export-oriented foreign directinvestment in the Caribbean Basin’, Journal of International Business Studies, 24(1),pp. 121–44.

boudier bensebaa 2005 economics of transition

Documents

countries of fdi

polarization of fdi

minants of fdi

fdi targets

choice of location

regional location

s oneway fdi model

emerging countries