MNB WORKING PAPERS 2005/7. Location of manufacturing FDI in Hungary: How important are inter-company relationships? GÁBOR BÉKÉS

Transcription

1 MNB WORKING PAPERS 2005/7 GÁBOR BÉKÉS Location of manufactuing FDI in Hungay: How impotant ae inte-company elationships?

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3 Location of manufactuing FDI in Hungay: How impotant ae inte-company elationships? Octobe, 2005

4 The MNB Woking Pape seies includes studies that ae aimed to be of inteest to the academic community, as well as eseaches in cental banks and elsewhee. Stating fom 9/2005, aticles undego a efeeeing pocess, and thei publication is supevised by an editoial boad. The pupose of publishing the Woking Pape seies is to stimulate comments and suggestions to the wok pepaed within the Magya Nemzeti Bank. Citations should efe to a Magya Nemzeti Bank Woking Pape. The views expessed ae those of the authos and do not necessaily eflect the official view of the Bank. Woking Papes 2005/7 Location of manufactuing FDI in Hungay: How impotant ae inte-company elationships? (A feldolgozóipai FDI elhelyezkedése Magyaoszágon: Mennyie fontosak a cégek közötti kapcsolatok?) Witten by: Gábo Békés* Magya Nemzeti Bank Szabadság té 8 9, H 1850 Budapest ISSN (online) * Institute of Economics Hungaian Academy of Sciences and Cental Euopean Univesity, coesponding addess: Budapest, Budaösi út 45, 1112 Hungay. bekes@econ.coe.hu

5 Contents Abstact 4 1. Intoduction 5 2. Related liteatue 7 3. Theoetical famewok Desciption of the data and vaiables The copoate dataset Equation fo estimation Fom fim data to access vaiables Wages and othe vaiables Estimation methods and esults Conditional logit model Results with the conditional logit model Non-independent eos and the nested logit Nested logit esults Count data methods and esults Compaisons though time and industy New fims vesus acquisitions Conclusions and futue eseach 29 Acknowledgements 31 Refeences Appendix Fims vesus plants Coections to the data 35 3

6 Abstact In a new economic geogaphy famewok with input-output linkages, this study analyses decisions made by foeign fims about thei location within Hungay. These fim-to-fim contacts ae modelled by ceating seveal copoate custome and supplie access measues fo all new foeign copoations. In ode to see the impact of these vaiables othe foces of agglomeation such as distance to Westen Euopean makets and dispesion foces such as high wages ae taken into account. Investigation is caied out on a small-to-medium sized Euopean economy that has ust gone though economic tansition involving almost unpecedented apid maket libealisation. A ich dataset of copoate tax etuns of Hungaian fims between 1992 and 2002 as well as annual labo suveys ae used to get location, sales and wage data. Vaious econometic specifications of both discete choice and count data models ae applied to povide obustness of esults that may be cucial when woking with fim level data. JEL classification: F23, R3, R12, C35. Keywods: economic geogaphy, industial location, FDI, egional policy, discete choice models. Összefoglalás A tanulmány az ú földazi közgazdaságtan keetei között vizsgála a külföldi befektetõk telephelyválasztását egy oszágon belül. Egy monopolisztikus vesenye épülõ egyszeû modellben bemutata az ágazati kapcsolatok közvetlen használatát, és diszkét függõ változóú modellekben megbecsüli a telephelyválasztást meghatáozó tényezõk szeepét. A klasszikus befektetést vonzó változók mellett, mint az alacsony béek, a felett infastuktúa és a nemzetközi hatáokhoz való közelség, elemzi a végsõ fogyasztók és a vállalati patneekhez való közelség szeepét. Robusztus eedmények biztosítása édekében a feltételes és beágyazott (nested) logit, Poisson, illetve negatív binomiális egessziós modellek keetében a tanulmány számos specifikációt tesztel. Az adatok az APEH-panel adatbázisából számaznak, amely tatalmazza az összes magyaoszági vállalatot. A dolgozat az közötti idõszakban elentõs külföldi tuladonnal léteött feldolgozóipai cégek telephely-választási döntéseit elemzi. 4

7 1. Intoduction In Cental and Easten Euope, apid changes and estuctuing in manufactuing have taken place since 1990 and spatial inequality has isen substantially along with new investments by fims enteing counties peviously closed to foeignes. This peiod may be chaacteized by economic libealization and opening up makets to foeignes. Pio to 1989, thee has been hadly any foeign diect investment in Hungay, while by 2003, the FDI stock eached almost 50% of the GDP. One would expect that ealy investments ( ) favoued the capital city and the bodeline egions but then, capital spead out moe evenly in a small county like Hungay as time went by. Howeve, this has not been the case. Instead, agglomeation of investments and a spatial polaization have been visible phenomena in geneal and in most sectos of the industy. Fo example, atio of the GDP pe capita in the two ichest counties to the one in the two least developed counties ose fom 2.3 in 1993 to 2.9 in Consideing all 19 oughly similaly-sized counties in Hungay (i.e. taking out the lage capital egion of Budapest), ou data suggest that new investments wee often concentated in a few egions. Duing the peiod , the top 3 counties (out of 19) wee consistently esponsible fo 35%-40% of new foeign fim establishments. As a esult, iche counties (Vas, Gyõ-Moson-Sopon, o Feé) managed to incease thei shae of FDI ove time - as epoted in Table 1. Of couse, the capital city has attacted the lagest shae of FDI oveall. Duing the same peiod, Budapest attacted 1.18 new fims pe 1000 inhabitants while the same measue is less than 0.2 fo the wost pefoming two counties. The same patten is tue fo a set of counties in the egion: manufactuing of electonic devices by fims such as Flextonics in Cental and Easten Euope can be found in a faily naow band fom Noth Poland though the Czech Republic, West Slovakia, West and Cental Hungay down to Noth Slovenia and Coatia. 1 One eason behind this agglomeation tendency is that new fims have a high popensity to settle at places whee economic activities ae aleady established as suggested Ottaviano et al. (2003, p. 7.). But them why do fims like to settle at places that othe fims chose? Futhe, why has agglomeation of new investment been such a visible featue of development? Of couse, Alfed Mashall agued a centuy ago that cetain types of extenalities may explain such patten. Both in theoy and empiics, localization economies, aising fom the poximity of fims to each othe has often been found to be the main cause of agglomeations, industial clustes o even cities. Exploing one paticula featue of this extenality to explain agglomeation foces at wok, is the focus of this pape. We believe that a deepe undestanding of how foeign fims pick a paticula aea fo thei plant would help economic policy make a bette use of Euopean and national development esouces. In this pape, we analyse decisions made by foeign fims about thei location within Hungay in a new economic geogaphy famewok with input-output linkages. These fim-to-fim contacts ae modelled by ceating seveal copoate custome and supplie access measues fo fims based on fim level data. In ode to see the impact of these vaiables othe foces of agglomeation such as distance to Westen 1 Fo details see Bata (2003). 5

8 Magya Nemzeti Bank Euopean makets and dispesion foces such as high wages ae taken into account. Investigation is caied out on a small-to-medium sized Euopean economy that has ust gone though economic tansition involving almost unpecedented apid maket libealisation. Rich datasets of copoate tax etuns and annual labo suveys ae used to get location, sales and wage data. Vaious econometic specifications of both discete choice and count data models ae applied to povide obustness of esults that may be cucial when woking with fim level data. The pape is oganised as follows. Section two summaizes the elated liteatue analysing esults of fim location in geneal and FDI location in paticula. We pesent the theoetical backgound of location choice in section thee. Datasets and vaiables ae descibed in section fou discussing advantages and pitfalls of mico datasets as well as explaining the ceation of the access vaiables. Section five pesents the econometic methodologies along with all the esults and obustness checks. The last section concludes. 6

9 2. Related liteatue Befoe tuning to the theoetical backgound and the estimation of the model, let us biefly pesent a backgound of economic geogaphy and discuss some key empiical esults in location choice with a special emphasis on Euope. With the emegence of new economic geogaphy (o NEG) models, issues elated to the maked diffeence between developed and undedeveloped egions have been given a solid analytical famewok. 2 These models aim at uncoveing the essential easons behind both agglomeation and dispesion of economic activity. Thee ae many easons fo the concentation of poduction such as the attactiveness of sevicing a lage maket, the poximity to supplies of intemediate goods o vaious foms of technological spill-oves. Of couse, agglomeation foces do not pevail without boundaies, thee ae dispesion foces in action, too. High wages will make cetain wage-sensitive industies incapable to offset ising costs. These companies will at some point opt to locate in the othe egion. Although they will face much highe tansaction costs when selling to the lage (and iche) egion, poduction costs will be much lowe in the othe egion. Such new economic geogaphy models have been employed to explain location of oveall economic activity by Kugman (1991), industial clustes in Fuita et al. (1999, Ch. 16.) o location of vaious manufactuing sectos by Midelfat-Knavik et al. (2000). Both theoetical and empiical wok in this field ae centeed aound two key deteminants of location: agglomeation extenalities and maket access. These notions will play a cental ole in this study and hence, it is woth giving a bief account of the key ideas, emphasising the difficulties that often aise when one ties to disentangle vaious foces. 3 Agglomeation extenalities wee fist emphasised by Mashall, and fomalisation of most such extenalities may be found in Fuita et al. (1999, Ch. 16.). In most of the ealy models, labou migation was essential fo agglomeation foces to wok: an inceased population geneated geate demand inviting moe fims to settle in a lage city, and this allowed fo a lowe impot bill and hence, lowe living costs in geneal. Howeve, even in the long un, labou migation is athe low in Euope in nominal tems as well as compaed with the United States. Thus, anothe agglomeation foce was equied to explain the desie to co-locate in spite of low migation popensities. This explains why the incopoation of intecompany sales o in othe wods, input-output (I-O) linkages wee so impotant fo empiical wok. These linkages ty to captue tading costs between fims explicitly thus, povide a motive fo co-location. Of couse, thee ae othe well known easons fo agglomeation, dives of industial clusteing. One such eason that makes woth locating close to one anothe is the potential of knowledge spillove. This is tue fo human as well as physical capital. The attaction to wok close to othe people is noted in Mashall (1920) and the impotance of face to face communication is discussed in Leame and Stolpe (2000). As fo fims, poximity allows to exchange inventions while technology spilloves help incease 2 Fo details, see fo example Baldwin et al. (2003) o Ottaviano et al. (2003). 3 An excellent suvey of key hypotheses emeging fom models of new economic geogaphy and thei mixed empiical suppot can be found in Head and Maye (2004). 7

10 Magya Nemzeti Bank poductivity using othe fims knowledge. Anothe such agglomeation foce is labou pooling: fims enoy the pesence of a lage set of labou whee the specific knowledge equied by the fim, may ust be fished out easily (as in Amiti and Pissaides [2001]). Bay et al. (2003) emphasised that it is athe difficult to disentangle the agglomeation and the demonstation effect because of a eputation effect that makes it optimal to mimic each othes location decisions. In thei empiical study, demonstation is consideed to be a pat of co-location extenalities that is not explained by agglomeation vaiables such as R&D intensity (spillove), excess ob tunove (labou maket thickness). Of couse, infomation shaing and demonstation effects ae closely inteelated. As fo the access to makets, the key idea that fim location depends on the poximity of demand was intoduced a long ago, and Hais (1954) devised the simplest aggegate maket-potential function. Maket potential has been fist investigated in an intenational context; poximity to key makets and supplies has been explicitly featued in empiical woks explaining oveall economic activity o pe capita income. Redding and Venables (2004) ague that a county s wage level (poxied by pe capita income) is dependent on its capacity to each expot makets and to manage to get hold of the necessay intemediate goods cheaply. Head and Maye (2005) look at Japanese investments caied out in the Euopean Union. Results show that apat fom a vey impotant maket potential measue, a numbe of taditional explanatoy vaiables (e.g. taxes) and agglomeation vaiables tun to be significant as well. This pape looks at a naow location choice poblem, when fims may choose a site within a county only. Thee have been seveal papes dealing with location decisions of foeign investos within one county. Cozet et al. (2004) study location of FDI in Fance using a model of oligopolistic competition to simulate copoate choice of location. They find that fims of the same nationality like to goup togethe, locations close to home county ae chosen moe fequently, and some industies (like ca plants) have a stong tendency to agglomeate. Similaly, a study by Head and Ries (2001) looks at Japanese investments in the US and finds that fims belonging to the same keiitsu tend to settle close to each othe. Some studies consideed counties of simila size and population to Hungay. Baios et al. (2003) look at multinationals location choice in Ieland with special inteest in the ole of agglomeation foces as well as state suppot. They find that agglomeation foces contibuted substantially to location choices but poximity to mao pots and aipots was also helpful. Moe impotantly, they find evidence that highe public incentives in designated aeas have inceased the pobability of multinational investment. Figueiedo et al. (2002a) took Potuguese data to look at, among othe factos, the home field advantage. Anecdotal evidence confims that agglomeation foces ae active in tansition economies of Cental and Easten Euope. The pesence of industial clustes is an easy-to-spot featue of new manufactuing base in the egion, including the moto vehicle cluste in Noth-West Hungay, West Slovakia o South-West of the Czech Republic. Also, thee is some evidence showing that lage multinationals lued in thei usual supplies. 4 Results with data on these economies have ust stated to emege of late. Disdie and Maye (2004) compae Fench investment in the Westen and Easten pat of Euope. They 4 The latest example is Hyundai motos in Slovakia whee eight othe Koean fims announced to follow Hyundai. 8

11 Related liteatue find that location choice is positively influenced by local demand and poximity to Fance inceases the pobability of a given county being chosen. Cieslik (2003) uses a Poisson model on 50 Polish egions to find that poximity of key expot tagets, industy and sevice agglomeation, and oad netwok ae the impotant magnets fo foeign investment. As fo Hungay, Boudie-Bensebaa (2005) focuses on the agglomeation effect and estimates motives of location choice using egionally aggegated data. The distibution of the FDI stocks in counties ae found to be elated to labo conditions and manufactuing density and the numbe of existing entepises. Fazekas (2003) looks at the concentation of FDI fom a labou maket point of view to study what impact capital inflow had on the egional stuctue of the county. The pape finds that concentation of foeign-owned entepises is ust maginally highe than that of the domestically owned ones. Howeve, FEs ae concentated in a diffeent patten, being located closely to the Westen bode. The appoach of this pape is somewhat diffeent to Fazekas (2003) in that it investigates the agglomeation pattens of foeign fims only. 9

12 3. Theoetical famewok The theoetical famewok aims to emphasise business to business elations as a key dive of location decisions. The main elationship between any two fims is a potential of supplie-buye link, i.e. one fim s output is the intemediate good of anothe. Modelling and measuing this potential will be in the cente of this analysis. The model is using the classic ingedients of new economic geogaphy based on the monopolistic competition of Dixit and Stiglitz (1977) and fist pesented by Kugman (1991). One key aspect of fim-to-fim elationship hee is elated to input-output linkages that wee intoduced by Kugman and Venables (1995) in ode to model the fact that fims sell goods not only to consumes but to othe fims as well. This pape follows the concise display of multi-county and multi-industy model in Fuita et al. (1999, Chapte 15A). Thee is monopolistic competition in all sectos poducing a ange of diffeentiated goods. The pape focuses on manufactuing. The agicultue secto, which has been pesent in many simila models, will be ovelooked. Tue, a dispesion foce will be lost but in the lack of lage-scale migation, wages and local consume demand should be stong enough to foste agglomeation. Thee ae =1...R egions, =1...J industies, with n fims poducing a vaiety each of industy in egion. Pofit fo each fim depends on fim- and industy-level chaacteistics. Fim-level chaacteistics such as technology advantage ove industy pees and quality of management ae unobseved. Howeve, these featues ae assumed to be independent fom the choice of location. Anothe deteminant of a given fim s pofit depends on such industy featues as (aveage) technology, skill equiements, tansaction costs and location of makets. These ae indeed egion-dependent factos. Thus, pofit fo fim i in industy and egion will come fom these two tems and we assume additive sepaability: π ( ) i = π i + Π (1) Since the focus is on location choice, the assumption of additive sepaability allows fo woking with Π only. Assume now that fixed cost of stating a new business is the same in all egions, and the cost of capital is unchanged though space as well this can be consideed as one key diffeence between national and intenational models. Fims pay taxes and eceive investment suppot. Howeve, in Hungay, local economic policy is not defined by counties but detemined at the settlement level, and egional tax incentives ae elative novelty, so it was assumed that egion specific state intevention is zeo. The pofit is simply: Π = ( p x mc x ) (2) The epesentative consume daws utility fom consuming a composite manufactuing good: U=CM μ. Howeve, the consume enoys seveal manufactuing goods, and the composite good consumed comes fom a constant elasticity of substitution (CES) subfunction of the available vaieties. 10

13 Theoetical famewok C M 1 1/ σ ( c ) = di i 1/(1 1/ σ ) The elasticity of substitution between goods is measued by σ. Theoetically it measues to what extent goods ae close to each othe, i.e. whethe consumes ae easily willing to eplace one with anothe. If it is small, poducts diffe, in case of σ =, the poducts ae homogenous, and the maket stuctue is identical to pefect competition. As it is the case in models following the Dixit-Stiglitz tadition, pofit maximisation yields a pice that equals maginal cost and a makup, : (3) p = mc Φ (4) In ou case, the makup depends on the elasticity of substitution. Assuming that fims have the same size, and thee ae N fims in egion, it can be shown that the makup is: Φ = σ σ 1+ ( σ 1) /N (5) Indeed, if two poducts ae close substitutes, the maket powe to set pices should be small, hence the low makup. It is assumed that thee is a lage enough numbe of fims, hence: Φ, i.e. the makup is not dependent on consumption. This assumption is cucial ( =Φ ) σ /( σ 1) fo it yields that mill-picing is optimal. Fims use a set of goods poduced by fims in othe industies that ae aggegated by a CES subutility function into a composite good. The intemediate good pice index, G denoting the minimum cost of puchasing a unit of this composite good, is a key vaiable in this setup fo fims benefit fom supplie poximity. If a geate quantity of necessay intemediate goods is poduced locally, less tanspotation cost will have to be paid. Hence, poduction costs will be lowe, too. This ceates a fowad linkage. Hee, the intemediate pice index is weighted aveage (with n l being the numbe of elevant fims 5 ) of f.o.b. pices (p l τ l_ that aleady include an icebeg type tanspot cost, τ l_ 1: (i.e. fo the home egion only τ l_ =1 ). G = R l= 1 n l ( p τ ) l l _ 1 σ 1 1 σ (6) This way of incopoating the pice index implies the love of vaiety effect. The intemediate pice index fo a fim in industy of egion is G. Input-output coefficients ae denoted by io i detemining the shae of industy in all output used by industy i. In a small county, industy buys goods and sevices fom aboad and the impot coefficient, io *, fo each industy gives the shae of a composite impoted good (piced G ). Since data come fom a complete national input-output (I-O) table, J io +io =1, J. W i=1 i * 5 Late, the numbe of fims may be eplaced with volume of output. 11

14 Magya Nemzeti Bank i ioi i io ( G ) ( G i J GP = W) i= 1 (7) Fims sell thei poduct to consumes and fims who use othe fims output as thei input. This latte gives ise to a system of input-ouput linkages a key agglomeation foce. Now the maginal cost function of a epesentative fim in industy and egion may be defined as follows: a mc = w ( GP ) ( b μ δ ) (8) whee w is the nominal wage, GP is the composite pice index of intemediate goods and b is a vecto of othe location dependent non-wage factos of the locally consumed poduction such as communication infastuctue. Let us define ql, as demand in a egion l fo a unit of industy output, poduced in egion. Demand can be deived fom the CES utility: q l = ( p ) l σ ( τ l 1 σ σ 1 l _ ) El ( Gl ) l Expenditue on the th industial goods fo a given egion (El ) comes fom two souces: consumes (who spend a μl faction of thei income on l egion, industy goods) and othe fims coming fom all industies. E l = μ l INC l + J i= 1 io i X i (9) In equilibium, X, the supply of an industy in egion will be equal to demand fom Hungay and the est of the wold. R = l=1 X q + QW l (10) whee QW epesents foeign demand. Unlike in Fuita et al. (1999), this pape does not intend to end up with a set of equations and simulate esults. Instead of a geneal equilibium appoach we need to be shot sighted and conside a patial equilibium without dynamic effects of an investment. In the long un equilibium, pices ae adusted taking extenalities into account. Fo example, wages o land pices will eflect benefits of agglomeation and lowe pices in one egion will only signal pooe cicumstances. In the shot un, disequilibia may exist and enty of fims (bidding up wages and input pices) shall be consideed as a foce to bing pices close to thei equilibium value. The main goal of this execise is to obtain a copoate pofit function that will be linked to the settlement decisions of fims in the empiical wok. 12

15 Theoetical famewok 13 So, the pofit function can now be ewitten: (11) whee is a monotonically deceasing function of the industy specific elasticity of substitution, σ. Note, that this measue is industy-dependent only, and hence will be empiically ielevant. Let us define the aggegate demand vaiable, AD as (12) Note that the way demand is set up ceates a backwad linkage: fims want to be close to thei makets and potential customes. So, the pofit function is: (13) The pofit function captues both key notions fomely intoduced. Access to makets is incopoated both fo fims and fo final consumes. Agglomeation economies will be captued by some b vaiables as well as some of the access vaiables. The way demand is set up allows the intoduction of some of the key business to business elationships. ( ) [ ] a AD GP w ψ = Π σ δ μ 1 ) ( ) ( b + + = = = u x l l i l l u l R l QW G X io INC AD 1 _ _ 1 ) ( ) ( ) ( : τ μ τ σ σ σ Φ Φ = ψ 1 ) 1)( ( : + Φ Φ = Π = u x l l u l R l QW G E mc mc 1 _ 1 1 _ 1 ) ( ) ( ) ( ) ( 1) ( τ τ σ σ σ

16 4. Desciption of the data and vaiables An impotant contibution of this pape is the application of dataset that includes all fims thoughout thei lifetime including the yea of enty and exit. Thus, final copoate decisions may be looked at instead of announcement of investment poects that may o may not have been ealised. Futhemoe, instead of estimations and aggegations, this dataset allows fo ceation of output vaiables based on the actual fim-level sales data only The copoate dataset Thee ae two key datasets in the study. The copoate dataset used hee, is based on annual balance sheet data submitted to the Tax Authoity (APEH). This vesion comes fom the Magya Nemzeti Bank. The APEH dataset contains infomation on all egisteed, double enty book-keeping fims. Data include industy code, size of employment, shae of foeign owneship and a county code. Data ae available annualy fo the peiod. The numbe of copoations vaies yea to yea, ising fom 57,862 in 1992 to 184,703 in The dataset was impoved by the Economics Depatment of the Magya Nemzeti Bank as well as the CEU Labo Poect. (Fo details on the data, see the Appendix.) In its tax epot, each company epots a sales figue that can be picked up fom its balance sheet attached to the eanings epot. Sales data fo a fim i opeating in industy egisteed in egion at time t is denoted by: x(t) (i). The yea of fim bith equals the yea of fist appeaance in the dataset, i.e. the fist yea of submitting a epot to the Tax Authoity. Fo this is compulsoy, thee should be little eo in measuing the enty date. Foeign owneship is defined wheneve the foeign shae in equity capital passes a 10% theshold. Fo foeign companies defined this way, the aveage foeign shae is vey high and esults ae quite obust to aising the theshold to 25%. Also, wheneve foeign owneship is low at the beginning, in most cases it will ise substantially afte the fist few yeas. Oveall, the dataset is composed of 5350 location settlements by fims with foeign owneship in manufactuing only. Only 4557 may be cetainly consideed as new investment athe than foeign acquisition, and this pape deals with new investments only. Industies ae gouped in sectos accoding to two-digit NACE codes. With meging some industies (e.g. clothing and leathe), and excluding food poduction, thee emain 15 sectos; Table 2. epots the main chaacteistics. As fo changes though time, the funitue (and othe misc.) industy faed the best and textiles the wost. Thee ae some coefficients that ae not estimated but taken fom othe souces: Input-output table comes fom Hungaian Statistics Office s publication on 1998 data [KSH (2001)]. This is the only I-O table available fo the time peiod used. Howeve, the assumption that input equiements pe secto have not geatly changed in a decade seems acceptable. The data indeed show that poduction is specialized, about half the value of output comes fom puchasing goods and sevices fom othe poduces. Out of domestic input, some 40% comes fom buying goods, 55% fom maket sevices (including constuction) and 5% fom non-maket sevices. 14

17 Desciption of the data and vaiables 4.2. Equation fo estimation The pofit function fo the econometic model is ceated as follows. Conside the pofit function (13), whee pofit is detemined by labou costs, aggegate demand, intemediate good pices, and othe cost factos, and take logs to get a linea elationship. ( w ) + μ ln( GP ) + δ (1 σ ) ln( b +ψ [ AD ] lnπ = a ln ) + (14) Aggegate demand (AD ) will be measued by final good demand, access to foeign demand and copoate maket access. Final good demand is poxied by puchasing powe of consumes that is measued by the vaiable INC, which is decomposed into the numbe of inhabitants, Pop and income pe capita, IPC. The intemediate good pice index (GP ) cannot be measued diectly, so it will have to be poxied by supplie access vaiables. Given the maket stuctue, the intemediate pice index will be negatively coelated with the supply of these goods. The vecto of cost factos (b (t) ) includes some basic featues of development that ae not industy specific. A moe developed county should yield lowe tansaction costs and hence, maginal cost of poduction. We use seveal such measue and look fo a positive elationship between development and location choice. As fo the labou maket, wage (t) measues the local wage. Wage vaiables wee calculated fom the LMS data and eflect (goss) labou costs that should be expected by a fim looking to settle in the given county. Finally, we need to intoduce the time dimension that has been so fa ovelooked. Explanatoy vaiables ae lagged one yea fo two easons. The economic ationale (see time-to-build models) is that fims may be assumed to spend a yea between investment decision and actual functioning (that is picked up by the data). The econometic suppot stems fom a equiement to ty to avoid endogeneity, and lagging will fee the model of simultaneity bias. We also need to assume that fims at time t consideing values of explanatoy vaiables at time t 1, pick a county independently of each othe. Agglomeation woks as fims locate close to othe fims that had settled peviously, but thee is no stategic inteaction between fims settling at time t. This is a necessay assumption fo using the logit model. Fo pasimonious notation, let us intoduce the vaiable ACC(m) that includes all access vaiables. Note that since ψ is not county dependent, it shall be dopped. As a esult, ou expected pofit function fo a fim i is: γ π ( t) ( i) = α1wage ( t 1) + α2inc( t 1) + β1acc( t 1) + b ( t 1) + ζ( t) ( i) (15) whee the eo tem, ou vaiables. ζ ( t) i ( ) includes all the non-obseved vaiables. Table 3 epots basic data on all 15

18 Magya Nemzeti Bank 4.3. Fom fim data to access vaiables In this section, ceation of vaiables, which ae used in estimations, is explained. Unit tanspot costs ae estimated by assuming a vey simple elationship: τ l _ p = distl _ p V (16) i.e. it depends on the distance and on the cost of tanspoting one dolla woth of good by one kilomete. All data efe to distance by ca, thus the oad netwok that is cucial fo tanspotation of goods is indeed taken into account. In eality we know little about coefficients of the elationship above. Studies with intenational data make use of the availability of coss-egional (i.e. intenational) figues fo tade. This allows explicitly to estimate tanspotation costs. Hee, it is assumed that shipping a good to 200 km costs twice the amount it does fo 100 km. Note, that this is highe than some estimates fo intenational shipment costs (e.g. Hummels [2000]). Howeve, ou vaiable includes all costs elated to doing business. The value of a typical package of industial output V = ($ / kg) on 1 km comes fom the Wold Bank database on intenational feight costs. Tue, these figues ae based on moe developed maket data, and aggegation will mask many featues. Howeve, it helps coect fo the fact that it is cheape to ship 100 woth of laptop PC than the same value of steel. (See Table 2b.) Thee ae vaious ways to measue distance between counties ( dist ), and hee a simple method is l _ p chosen. Fist, using the KSH T-STAR database on settlements, the most impotant city pe county is picked (i.e. with the lagest numbe of manufactuing plants). Note that picking the key city was staightfowad fo in all but one case, the lagest city was at least twice the size of the second. Second, distance between any two counties is defined by measuing the oad distance between the epesentative cities. It is assumed that goods ae tanspoted by tucks only, and that vehicles move at the same speed and costs ae indiffeent to oad quality. All access vaiables to be tested in fothcoming subsections ae based on output figues pe county and secto (Y (t) ). These numbes ae detemined by aggegating sales figues fom the balance sheet data fo all the elevant fims i (in industy and egion time t ): Y. = i x( t) ( i) Copoate access vaiables measue poximity to fims that may be elevant fo a new company, and the access vaiable is the sum of output by fims weighted by distance and shae in inte-company tade. Fom theoy, we need one vaiable to measue demand (MA ) and anothe one to poxy supplie access (SA ). Bea in mind, that although supplie and maket access vaiables ae compiled in a simila fashion, they measue diffeent types of vaiables. The maket access is about demand, while the supplie access is ust a poxy to (intemediate goods) pices. Futhe, both vaiables ae divided into two components: one to pick up access to local (intenal o within county) fims and anothe one fo non-local (extenal o outside the county) fims. The eason fo such dichotomy comes fom the suspicion that the effect of distance is not linea, and fims clusteed in one city o in a few cities close to each othe, enoy special agglomeation effects. 6 6 In a somewhat simila setup, Amiti and Javocik (2003) ceated such aggegate access vaiables. 16

19 Desciption of the data and vaiables Theoetically these ae the basic access vaiables we need. Howeve, thee may be (and as we will see it, thee is indeed) a stong coelation between SAloc and MAloc, and so is between SAnat and MAnat. One possible eason fo coelation between access vaiables is the fact that own industy output influences both the supplie and the maket access vaiable stongly. This stems fom the stuctue of commece between fims: companies tade the most with othe companies in the vey same industy. 7 On aveage, inta-industy tade amounts to one thid of total inte-company sales, and this exacebates coelation between the MAloc and SAloc vaiables. To emedy this, a new vaiable, IPloc is intoduced that measues own industy output only. (This of couse is also tue fo the non-local (national) vaiables.) Accodingly, copoate demand may be poxied by a local and a national (all egions except fo the local one) industy dependent maket access vaiables (local: MAloc, national: MAnat ). J R i i i = + = Yl MA λ1maloc λ 2MAnat io λ ( ) + ( 1 Y λ 2 i l τl _ In a simila spiit, the intemediate good pice index is poxied by two supplie access vaiables: ) (17) J R i i i = + = Yl SA ϑ SAloc ϑ2 SAnat io ϑ ( ) + ( 1 Y ϑ 2 i l 1+ τl 1 _ Impotantly, fo equations and we limit the input-output coefficients such that i. Fo cases when i=, IP is intoduced to measues own industy output only. R = + = Yl IP ι IPloc ι2ipnat io ϑ ( ) + ( 1 Y ϑ2 l 1+ τl 1 _ ) ) (18) (19) Access to foeign makets influencing both demand and intemediate good pices, is measued by a single foeign access vaiable (FMA ). This takes into account that expot is a cucial deteminant of the evenue of Hungaian fims and the aveage impot shae eached 34% fo manufactuing. By the theoy, the diect maket access to foeign (i.e. in counties n=1,2...n) fims and customes should be taken into account. 8 Howeve, due to data limitation poblems, this pape poxies access to foeign makets by taking into account the distance to the key expot bodes. N J+ K N N 4 INCn i Yn ts FMA = + io n 1 τ n _ i n 1 τ = + + n _ n 1+ τ n n _ (20) whee ts n is the shaes of tade to the n th diection. 9 7 This featue makes the use of models with two sectos, such as upsteam and downsteam industies, impossible. 8 Amiti and Javocik (2003) face the same challenge fo Chinese subsidiaies of multinational fims that typically poduce a geat deal of thei output fo foeign makets. In thei pape, access to foeign makets is poxied by the taiff ate but Euopean fee tade in manufactued goods makes this unnecessay. 9 We used distance to the bodes: West/Austia: Hegyeshalom; South/Coatia: Letenye; Noth/Slovakia: Komáom, East/Ukaine: Záhony, Aipot: Feihegy/Budapest. 17

20 Magya Nemzeti Bank Business access o BA picks up access to sevices such as banking, accounting o lodging, as a special deteminant of poduction costs. Fo sevices ae likely to be used locally, only conside access to local business sevices. BA = BAloc = K k io Y k k (21) whee k includes vaious sevice sectos of the economy Wages and othe vaiables The cost of labo may be a cucial dispesion foce and hence, its caeful modeling is impotant. To get detailed wage data, a lage employe-employee dataset is used that comes fom annual Labou Maket Suvey (LMS) data compiled by the Ministy of Labou, containing employment data on a sample of some employees pe yea. Employees ae picked independently of thei employes and the lage sample size allows fo annual coveage of all industies in almost all counties. This dataset allows to geneate county level aveage wages fo evey yea and county (wage (t) ). Using the same annual labou suvey, anothe labo cost vaiable, wage_ind(t) is geneated by aveaging wages of employees in a given county as well as industial secto. Out of the 3000 possible industycounty-yea combinations, we wee able to ceate 2737 industy specific wages diectly fom the data, while estimated the emaining 263 wage figues. Note that fo such industy-yea-county combinations, hadly any FDI investment has taken place they wee mostly used as countefactuals fo unning the logistic egessions. Aveage egional and industy specific wages wee ceated by weighting fim level (goss) wage infomation by the size of fims (employment) so as to geneate a wage level, a fim may expect when choosing a location. Fo evey employee thee is a desciption of the ob, and this allows to ceate a special blue-colla wage fo (almost) evey industy and county: wage_bc(t) and hence, wage_bc (t) may be used togethe with wage_ind(t). In this pape a few measues of development ae chosen to take cae of mao secto independent vaiables 10. Data come fom the egional database of the Cental Statistics Office. Size_of_oad_netwok measues the size of national oad netwok within the given county and it is equal to size (in kilometes) of all national oad (including motoways) divided by the aea of the county (in km 2 ). Note that thee has been little change in the size of the netwok thoughout the obseved peiod, the total size ose by about 3%. Size_of_telephone_netwok shows the numbe of telephone lines within the given county. This measues the numbe of landline stations pe county. This is a fequently used vaiable to poxy development of the infastuctue and thus, non-tanspotation linked tansaction costs. Note howeve, that as a esult of widespead use of mobile phones, this measue may have ust tuned to be a poo poxy by now. Numbe_of_college_students epesents numbe of students enolled in highe education at institutions within the given county. This should poxy the abundance of management and R&D knowledge in the county. 10 In a elated pape (Békés and Muaközy [2005]) seveal moe vaiables of local development and municipal policy ae tested. 18

21 Desciption of the data and vaiables In addition to measues of development, population_density indicates the size of population divided by size of the aea of the county and it will pick up an agglomeation extenality: it may be cheape to sell poducts when people ae close to each othe. Howeve, a negative sign would suggest that this ubanization effect is outweighed by highe land pices. 19

22 5. Estimation methods and esults Fist, conditional logit (CL) models will be estimated to study the influence of input-output linkages, labou maket conditions and maket access on investment decisions in Hungay. A key achievement that allows fo such a stuctue to be used hee is the Random Utility Maximisation famewok of McFadden (1974). In this famewok, fims ae assumed to make decisions maximising expected pofits, but given the scacity of infomation and eos made by analysts, the maximisation pocedue pe se is less than pefect. Thus, pofit (o utility fo consumes) is a andom function of explanatoy vaiables Conditional logit model The methodology widely applied in spatial pobability choice modelling is the conditional logit model based on Calton (1983). Decision pobabilities ae modelled in a patial equilibium setting with agents pusuing pofit maximization behavio. Thus, they maximise a pofit function like (15) subect to uncetainty. Apat fom the obseved chaacteistics of fims, such as secto and location (enteing the pofit equation), unobseved locational chaacteistics, measuement eos o impope maximization will detemine actual pofits. Note, that we do not obseve eithe deived o actual pofits, but peceive locational decisions of fims. 12 The explained vaiable is the location choice of fims so the choice vaiable is 1 if the investment took place in that paticula county and 0 fo the emaining 19 counties. Taking all potential effects into account, a fim i (whee i {1,...,N}) of secto (whee {1,...,J}) locating in egion (whee {1,...,R}) will attain a pofit level dependent on vaious industy and egion dependent vaiables. Impotantly, not all of these vaiables matte, as the choice of egion is independent on individual fim o industy chaacteistics. Thus, if agents maximise expected utility in this patial equilibium setting, the numbe of fims in a egion is elated to the expected pofit, as laid down in the pofit function. The pofit equation (15) in pasimonious fom fo a fim i in industy and egion is: π ( )( i) = γ b ( ) + λ d ( ) + ε ( )( i) t t t t (22) In ode to be able to use esults of McFadden (1974), we need to assume that the eo tem, ( ), ε ( t) i is independently distibuted acoss and i, and has a type I exteme value (o Gumbel) distibution. The eo tem eflects unobseved tems as well those that depend on individual fims. A cucial assumption is that unobseved chaacteistics do not cause coelation, i.e. eos ae independent of each othe. In othe wods, independence hee equies that the eo fo one altenative povides no additional infomation about the eo fo anothe one. It is likely that this assumption would not hold vey well fo the data but the geneality of the CL model allows fo a detailed investigation. (Fo details and some emedies, see section 5.3.) 11 Fo details, see Maddala (1983), Tain (2003, Chapte 3.). 12 In the copoate database thee ae of couse values fo pofit. Howeve, fo multinational companies they ae heavily distoted by tansfe picing as well as vaious gants and incentives. 20

23 Estimation methods and esults Fo evey spatial option, the investo will compae expected pofits and choose egion, povided that the following condition is fulfilled fo l : pob[ π ( ) < π ( l)] = pob[ ε < ε + A A + γ b + λ d γ b λ d i i i il l l l ] (23) If this is the case, it can be posited that the investo s pobability of selecting location, povided she opted to invest in secto is: P = P exp( γ b+ λ d ) = R exp( γ b + λ d l = 1 l l ) (24) Estimation is caied out by maximising the log-likelihood: J log L = n log P R = 1 = 1 (25) whee n denotes the numbe of investments caied out in secto of egion. In most specifications, fixed effects ae added to pick up possible level shifts caused by some omitted vaiables such as economic policy. As a esult, (22) would become: π ( t) ( i) = δ + γ b ( t) + λ d( t) + ε ( t) ( i) (26) whee δ ae location specific dummy vaiables. County level as well as NUTS2 egion level dummies ae intoduced to the key equations. Note that coefficients ae appoximations of the elasticity of the pobability of choosing a paticula county fo the aveage investo. 13 Fo example, consideing the most basic setup of specifications, a 10% incease in the local own industy access vaiable (o 10% ise in the output of the aveage fim o a 10% incease in the numbe of fims) would aise the pobability of choosing that county by 2% Results with the conditional logit model Results with conditional logit ae epoted in Table 4. In ode to contol fo unobseved county diffeences such as those stemming fom fist natue geogaphy, county o egion fixed effects (choice specific constants) wee intoduced. To contol fo the specific case of Budapest, a capital dummy was added to the egional fixed effects. This had little effect on copoate access vaiables confiming the obustness of esults. Howeve, many explanatoy vaiables, which depend upon location only, change little ove time, and thus, would loose significance in due couse. By the basic specification (CL1), demand vaiables such as pe capita income and size of population ente with the expected positive sign, while highe wages ae associated with a lowe likelihood of fim location. 13 It can be shown that tue coefficients ae (1-p*) times the estimated figues, whee p* is the aveage pobability of choosing a egion. Hee, p*=1/20=0.05. Remembe, that figues must be taken with cae fo the logit estimation is caied out with a nomalization of the vaiance of the eo tem. 21

24 Magya Nemzeti Bank The access to own industy output (IPloc) is stongly significant and so is the national (extenal) maket access vaiable (MAnat ), o the local (intenal) supplie access (SAloc). Access to business sevices is also seem to matte fo fims. These suggest that input-output linkages ae impotant deteminants of location choice. 14 Oveall, the local pesence of own industy is one of the most obust deteminants of fim location: camakes will ty to settle whee othe fims in the moto vehicle industy ae settled. Local supplies outside one s industy matte but seem less impotant. The esult that the national maket access is always positive and significant and positive suggests that fims would want to settle close to non-own industy customes, i.e. a steelmake will conside all potential copoate customes when deciding about location. At the same time, local maket access (MAloc) and national supplie access (SAnat ) ente with a negative sign, and so does national own industy output. Othe specifications confim these esults and seveal coefficients, such as (IPnat ), emain supisingly obust. These esults contadict theoetical pedictions but thee may be seveal explanations fo such esult. Note howeve, when access vaiables wee simply aggegated into a local and a national copoate access vaiable, both enteed with a significantly positive sign suggesting that oveall, input-output linkages outweighed maket cowding. 15 Fist, the coelation between supplie and maket access vaiables is stong (see table 6) and this may have emained a poblem despite pevious effots. 16 Note that this is not a unique poblem of this study, seveal pevious empiical woks with both supplie and maket access vaiables faced this coelation issue (Redding and Venables [2004]). In any case, multicollineaity is mostly a small sample issue, and we believe that ou dataset is lage enough to be able to disegad it. Futhe, when dopping one vaiable, the sign of othes would not change. Second, these vaiables may pick up the impact of some negative extenality of fim pesence. A pime suspect is competition o maket stuctue in geneal, which was left out given the infinite numbe assumption and the fixed makup esult of monopolistic competition type NEG models. Fo example, the negative sign on the access to national (extenal) output of the own industy (IPnat ), seems to suggest some sot of a competition effect outweighing any agglomeation effect. A negative sign may indicate such dispesion foce: i.e. it is good to have simila fims close, but pesence of too many fims in the neighbohood leads to maket competition and unde monopolistic competition, moe vaieties imply lowe pofits. At the some token, vaiables that ente with a positive sign may captue some othe foces. Indeed, industies like to cluste fo othe easons than input-output linkages as it is suppoted by the stong significance of the industial output vaiable of the actual secto (IPloc). One must emembe that it is impossible to sepaate the key motives, such as labou pooling, knowledge spillove o a decease in business costs due to infomation shaing. Despite ou effot to filte out co-location due to supplie linkages, these poblems emain impotant. 14 Thee ae seveal desciptive evidence that suggest that supplie contacts ae known to have been an impotant facto in the egion.fo example, in Hungay, supplies to the Suzuki ca plant ae mostly settled within a close poximity to Suzuki, often in the same county. 15 Futhe esults ae available fom the autho on equest. 16 One potential eason fo such esult may be non-lineaity in the data. To see this, I fist looked at the access vaiables (in logs) and found that thei distibution has a one-peaked distibution that looks not vey diffeent fom a lognomal distibution. Second, quadatic tems wee included to captue some sot of a hidden effect. It tuned out that some quadatic vaiables wee significant but they had no influence on any othe vaiable. 22

25 Estimation methods and esults Thus, esults suggest that within a small aea such as the county, agglomeation and input-output linkages ae moe impotant (as captued by a stongly positive IPloc vaiable), while maket cowding outweighs these positive extenalities fo othe counties in poximity (negative IPnat ). Pevious empiical wok suggests that one has to appoach the impact of labou maket on location choice with geat cae. The theoetical pediction of the wage coefficient is clea, wages ae positively elated to costs and hence a negative sign would suggest that high wages dete fim location. Howeve, the empiical evidence is mixed with a slight leaning towads the opposite sign. 17 In the basic specification (CL1), the wage vaiable (wage (t) ) entes significantly with the expected negative sign confiming pedictions of the theoy. Howeve, fo othe cases, the vaiable looses its significance and often, its sign changes. Thee may be seveal explanations, this pape undelines ust two such easons. Fist, vaious industies use diffeent types of labou in tems of skills, and hence, the industy mix of a egion may o may not influence the aggegate wage vaiable. Second, individual industies use diffeent types of labou in diffeent shaes. The shae of blue-colla wokes may vay a geat deal among sectos and thei wage may diffe geatly depending on how skilled they ae. Thus, in econometic models like those of this pape, wages may well eflect an industy bias as well as a skill bias. An insignificant o a positive coefficient may ust imply that investos ae binging in supeio technology and hence, equie moe skilled and educated (i.e. moe expensive) sot of labou eflected in highe wages. All specifications using industy specific aveage wages as blue-colla labou costs suggest that both vaiables ente significantly. A negative sign of wage_ind(t) confims the theoetically negative effect of high wage costs, while the positive sign of wage_bc(t) points to the notion that the skill bias impotant fo blue-colla wokes. Some specifications include non-industy dependent vaiables of b (t) such as the size of telephone o oad netwok, both being positively elated to fim location. This confims geneally held views that bette infastuctue is key to attact FDI. The agglomeation vaiable of population density entes with a significantly positive coefficient, too. The numbe of college students, as a poxy of labou make quality and eseach activity in geneal is also positively elated to fim location. One simple possible measue fo agglomeation at the custome level, is the population density. Its sign is not staightfowad. On the one hand, a moe dense aea allows fo lowe tanspotation costs within the county, but on the othe, it may lead to lowe land pices and hence lowe the cost of the investment. Results of the conditional logit ae athe ambiguous (but othe models suggest a positive elationship). Fo these vaiables, coss coelations ae inteesting to look at. Distance to expot destinations in negatively coelated with most development elated vaiable. Fo ove 80% of expots goes to Westen Euope, this confims a stong East-West division in Hungay. It is also clea, that Budapest is special in tems of these vaiables (that is not the case fo manufactuing based vaiables). 17 Fo example, in Figueiedo et al. (2002b) local wage has the expected sign, while in Holl (2004), the wage coefficient is insignificant. Thee ae vaious explanations. Fo example, Figueiedo et al. (2002a) ague that fims conside the wage level as a deteminant to locate in a cheape county like Potugal (o even moe so, Hungay) but within the county, wage has no effect. 23

26 Magya Nemzeti Bank 5.3. Non-independent eos and the nested logit The conditional logit modelling has some impotant limitations. An impotant estiction fo CL models is p ( y ) / p ( y ) = exp(( y y ) β) h h h (27) so that elative pobabilities fo any two altenatives depend only on the attibutes of those two altenatives (Wooldidge [2002, p. 501]). This is called the assumption of Independence of Ielevant Altenatives (IIA). In ou case, this posits that all locations ae consideed simila (having contolled fo explanatoy vaiables) by the decision making agent, yielding independent eos acoss individuals and choices. When IIA is assumed, an investo will look at all egions as equally potential places fo investment. Thus complex choice scenaios cannot be included. Indeed unobseved site chaacteistics (such as actual geogaphy) may well give way to coelation acoss choices. To check whethe the IIA assumption is stong enough, Hausman tests wee un (Hausman and McFadden [1984]) fo seven NUTS2 egions. Results (epoted in Table 7.) show that the IIA assumptions almost always fail at the 1% level, suggesting that a moe complex stuctue should be used. As is fequent fo such execise, asymptotic assumptions of the Hausman test fail fo some occasions and hence, the genealized Hausman test was applied. Given that thee is no theoetical suppot fo having seven egions, so an altenative stuctue with thee lage egions (West, Cental, East) was dawn, and the tests wee un only to indicate that IIA fails univesally fo such tee-stuctue. One possible way to contol fo violations of the IIA assumption is to intoduce dummy vaiables fo each individual choice as suggested by Tain (1986). Indeed, seveal specifications wee un with fixed effects. To see if the intoduction of fixed effects solved the poblem, Hausman tests wee e-un fo a fixed effect specification. It did not solve the poblem, all conditional logit stuctues may be eected fo violating IIA assumptions. This situation, often appeaing in execises simila to ou own, equies the nested logit model to be called upon. The nested logit model uses the same pofit function as the conditional logit (15) but woks with a decision tee. The fim now fist picks a egion out of uppe level altenatives u, and then chooses a county within the aleady selected egion, out of lowe level altenatives,. Impotantly, no assumption on a twostep decision-making is necessay. It is enough to believe that cetain counties ae competing moe closely than othes. Location pobability in a county, depends on pobability of location in a egion (u, uppe level altenatives) times the pobability of location in a county (m, lowe level altenatives) in the given egion: P = P P u u u (28) NNNL P = exp( β Zu) / exp( β Z un u n u ) (29) 24

27 Estimation methods and esults whee Z explains the choice of an uppe level (egion) altenative in the conditional logit case β Z = γ b + λ d. In this last equation, the inclusive value, (30), will tell us if the nest helps. Fom Maddala (1983), we know that and when, the NL collapses to CL, while if, the uppe nest mattes only, i.e. fims choose a county andomly within the selected egion. It is impotant to stick to the RUM famewok hee as well, so a andom utility maximization consistent nested logit had to be applied (Heiss [2002]). As a esult, deteministic utilities must be scaled by the invese of the IV m paametes ( Pu = exp( α Wu + ξu IVu ) / exp( α Wm + ξm IV IV = ln( n u exp( β Zun )) 0 ξ 1 ξ m =1 ξ m acoss nests but allows the intepetation of β Z as RUM model. m ξ m = 0 ) in the conditional utility. This implies diffeent scaling of the utilities m ) RUMNL P = exp( β Zu / ξm ) / exp( β Z un u n u ) (31) 5.4. Nested logit esults Thee ae two natual nests: the seven NUTS2 egions, thee boad geogaphical aeas: East, Cental and West as well as ou pefeed 4 egions of East, West, South and the capital plus its neighbouhood. Results, epoted in Table 5, povide solid suppot fo many of ou pevious esults. Accoding to specification NL1, the basic vaiables: pe capita income, size, local and national copoate access, business sevices access and wages, all ente significantly and with the expected sign. With disaggegated vaiables (specifications NL2-NL5), own industy output emains one of the best pefoming vaiables along with national (extenal) maket access. Bette local (intenal) supplie access emains a point of attaction, too. National (extenal) access to supplies and the own industy emain to ente with the negative sign. Othe explanatoy vaiables loose o gain significance depending on the nest. Specification test of the nested logit model is based on the values of the inclusive value paametes. The LR test of homoskedasticity (all values equal one) is clealy eected fo all specifications. No single IV m is eve close to the unity, suggesting that all pats of the nest is well waanted. Howeve, geate than unity figues in geneal indicate some specification poblem of the andom utility famewok. We checked fo seveal possible nests Table 5 epots esults fo thee such nest but failed to get inclusive values at o below unity. Once again, the model is likely to be misspecified, although Tain (2003, Chapte 4.) discusses studies that pove that fo seveal cases, RUM may well be consistent with IV values above one Count data methods and esults A geat advantage of CL appoach is its diect link with andom utility maximisation. Howeve, thee may be seveal specification poblems with the conditional logit model. The IIA assumption fails and the 25

28 Magya Nemzeti Bank choice of a cetain nested logit specification may seem somewhat abitay. Thus, one can apply count data models to see obustness of esults. This comes with an additional advantage: the easy inclusion of time dummies. Indeed, duing tansition, thee may have been impotant changes ove time such as shifts in public policy affecting egions diffeently. In an effot to check obustness of CLM, count data models ae used in this section, with the dependent vaiable epesenting the numbe o fequency of a paticula event, in ou case, the numbe of investments in a paticula county. In these models, coefficients explain why x% moe poects took place in county A elative to county B. Define n(t) as the numbe of FDI investments in industy, egion and time t. The explanatoy vaiables ae exactly the same as used in the pevious sections. P( Y ( t) = n) = exp( λ) λn / n! (32) Impotantly, Figueiedo et al. (2004) shows that the conditional logit equation as well as the Poisson model may stem fom the same andom utility maximisation model when fim-level chaacteistics ae teated in a discete fashion (such as opeation in an industy). Altenative to the CL model, we can assume that n(t) is the explained vaiable and n (t) ae independently Poisson distibuted with n ( t) = λ ( t) = exp( a d + γ b ( t) + λ d( t)) whee d ae dummy vaiables indicating if a fim is in industy. 18 Fo evey yea, fim enty data wee aggegated by industy and county, and Poisson egessions wee un with the same set of explanatoy vaiables used at logistic egessions (see Table 8). As expected, esults wee geneally but not always confimed. Own industy output, once again poved to be one of the best pefoming vaiables with a coefficient close to 0.2, along with national (extenal) maket access. Howeve, supplie access vaiables swapped signs compaed to logistic egessions. Othe explanatoy vaiables, such as distance fom bodes pefomed well, with even the numbe of college students making a diffeence. In a Poisson model context, the oad netwok was unimpotant while population density enteed with a significantly positive vaiable suggesting the pesence of some ubanization economies. The Poisson model has the advantage of being closely elated to the conditional logit, but it assumes that the conditional vaiance of the dependent vaiable, λ equals the conditional mean of λ. Howeve, equidispesion is ae popety in eality, and fo most cases, the vaiance is lage than the mean. Ovedispesion may be teated, but in a moe geneal, negative binomial model that allows to test the null hypothesis of equidispesion. 19 Given thei easy applicability, no wonde that both the Poisson and the negative binomial model have been used in location eseach (e.g. Basile [2004]). (33) 18 Moeove, Figueiedo et al. (2004, p. 203.) shows that the Poisson concentated log likelihood is identical to the conditional logit likelihood with some constaints. 19 Impotantly, the negative binomial model yields moe efficient test statistics and pevents us fom dawing ovely optimistic conclusions (see Cameon and Tivedi [1998]). 26

29 Estimation methods and esults The negative binomial distibution may be consideed as a genealized Poisson, whee the mean does not equal the vaiance. This deviation is epesented with a dispesion paamete, α. The case with α=0 coesponds to equidispesion, and in that case the model collapses into a Poisson model. Specification tests (LR test with one sided χ 2 statistics) suggested that the Poisson model is misspecified. Howeve, esults, epoted as specification CNT5 and CNT6, suggest significance. In many cases even the magnitude of coefficients fo the negative binomial ae identical to those of the Poisson model despite the failue of the LR test. This obustness is not unusual in the liteatue, fo example Smith and Floida (1994) finds a simila patten fo Poisson, negative binomial and even fo the tobit model Compaisons though time and industy So fa we have pooled data fo both yeas and industies. It is inteesting to see to what extent coefficients change though peiods in time and acoss goups of industies. 20 To see how vaiables evolved though time, fixed effect conditional logit egessions wee un fo thee peiods: , and Many coefficients, including those elated to the input-output linkages changed little though time. Howeve, high wages seems to have been moe of a deteent in ealy nineties with the coefficient loosing significance afte 2000 o skill-content, undetected by industy specific wages, has become moe impotant of late. Regession esults would be diffeent if un secto by secto. Robustness of pevious esults was fist checked by unning egessions leaving out one industy at a time. Results vaied maginally only. Second, some industies wee gouped into two categoies: light industy (e.g. textile, clothing, etc.) and electonics/equipment (inc. electic machiney, audio-video manufactuing, etc.), and egessions wee un fo one goup at a time. Results ae boadly obust but some vay substantially. Light industies slightly pefe wealthie sites pobably given a lowe expot content in thei sales. Similaly, distance fom expot destinations was moe impotant fo the equipment/machiney/vehicles secto. Wages wee much moe impotant fo the light industies, wheeas highe skill-content sectos appeciated skills moe. Inteestingly, the national own industy output vaiable (IPnat ) tuned to be negative fo the light industy goup only, suggesting that nationwide competition was stonge fo lowe value added and/o less diffeentiated good poducing sectos. As a caveat hee, note that compaison within a logistic famewok is not diectly possible. In a logit egession, the vaiance of the eo tem cannot be estimated togethe with paametes and as thus, the vaiance tem is nomalized to one. As a esult, a diffeence in values may only be due to a diffeence in the vaiance of the eo tem. Hence a diffeence in the coefficient value may be meaningless New fims vesus acquisitions So fa, we have looked at locational deteminants of new fims only. We have data on foeign acquisitions that may eithe be consideed as pivatization deals o investment by a foeign fim in an existing 20 Results ae available on equest fom the autho. 27

30 Magya Nemzeti Bank Hungaian company. Given that a substantial effot has been invested into linking fims that changed legal status but emained the same company in essence, ou acquisition data include episodes when a fim exited and a new fim appeaed at the same aea with diffeent owneship but vey simila stuctue. Thee ae all togethe 870 foeign acquisitions, out of which some 200 being elated to pivatisation i.e. a foeign shae eplacing state o municipal owneship. Ou fixed effect conditional logit egessions wee un fo both goups: new fims and foeign acquisitions. The good news is that esults, in tems of sign and significance, ae almost always unchanged fo these goups, although some access vaiables loose significance. 28

31 6. Conclusions and futue eseach This pape focused on location decisions of foeign investos within one county, using econometic models with discete dependent vaiables that ae geneated fom a tax epot based dataset of Hungaian fims. The apid appeaance of foeign-owned manufactuing sites offeed a geat oppotunity: studying the geogaphic popeties of a lage numbe of new fims enteing a egion peviously closed to foeignes. Some conclusions may be dawn egading theoy and its empiical suppot as well as the validity of some methodologies. Taking a snapshot of the economy athe than modelling long un equilibium, one of ou aims has been to bing a widely used class of new economic geogaphy models to the data and investigate how well vaious channels of agglomeation and dispesion foces wok. In the pape a possible way was shown to link input-output linkage based NEG theoy and a tax epot based dataset building on vaiables that had been geneated out of fim level sales figues. In ode to see validity of esults, specifications of conditional logit, nested logit, Poisson and negative binomial models wee tested. Although specification tests suggested that econometic models have geneally been misspecified in one way o anothe, most coefficients kept thei espective sign thoughout specifications, and simila log likelihoods (o McFadden s pseudo R 2 measues, whee available) suggested that most specifications ae by and lage equally suppoted by data. Results that poved to be obust though discete choice and count data specifications suggest that thee is indeed an agglomeation effect fo companies in play and input-output linkages wok thei way though supplie and maket access poviding a key eason fo co-location. The impotance of industial clusteing has been obustly shown and some suppot of agglomeation extenalities was found as well. Access to fims opeating in the same industy as the new fim as well as poximity of potential customes thoughout the county seemed to be a pesistently impotant deteminant of location choice. This povides some empiical suppot to NEG models with input and output linkages. Howeve, some impotant difficulties have aisen. Fist, the fact that a lage shae of action is going on within the own industy suggests that disentangling vaious agglomeation foces within an industy has once again poved to be athe difficult. As a esult, when data pemits, one would pobably need to incease data esolution and leave two-digit industies (such as electonic equipment poduction) fo thee-digit sectos (e.g. medical equipment). Second, the unexpected sign of some access vaiables suggest that disegading maket stuctue and in paticula, competition, the effect of which may have been picked up by some access vaiables, is a gave weakness of the model. Indeed, we now eckon that competition must be studied moe diectly, allowing access vaiables to pick up less of maket cowding effects. 21 Wages have been impotant in explaining fim location. Howeve, unless industy specific wages ae used, the impact of labo costs ae mostly undetected. Futhe, the addition of blue-colla wage costs that eflect the heteogeneity (in skills and taining) of a elatively immobile and seemingly homogenous wokfoce impoves ou undestanding. 21 Unfotunately, modelling competition is athe difficult. Nevetheless, in the empiical liteatue one possible poxy used to captue the impact of maket stuctue is a vaiant of the Hefindahl index. 29

32 Magya Nemzeti Bank The expot distance measues ae ovewhelmingly significant with the expected negative sign in any specification. Fo a small and open economy this is not supising. Most govenments emphasise the constuction of mao East-West o Noth South coidos and the impotance of this notion is confimed by the stong significance of ou oad distance to bodes paamete. Howeve, positive coefficient of the oad netwok vaiable suggest that building oads within a county will foste FDI inflow as well. Finally, some policy conclusion may be dawn with caution. Fist, most of the industies do have a stong tendency to settle whee othe simila fims have aleady settled. Spending money on incentives to have them established elsewhee may be inefficient, and instead labou migation should be made easie, fo example via development of tempoay housing conditions. Futhe, subsidies to lage fims may be efficient as long as they lue in simila fims. Second, input-output linkages ae impotant. Thus, impoving the elationship between supplies and multinationals is key to fosteing moe investment. With a ecent expeience of loosing multinationals to non-eu Easten Euope and China, this may be eve moe impotant. Thid, othe explanatoy vaiables that wee found to be a significant ae telephone and oad netwok, confiming the widely held view on the impotance of local infastuctue Howeve, one must bea in mind that seveal geneal equilibium NEG models would show how constuction of motoways may have an advese effects in the long un. See Baldwin et al. (2003) fo theoy and Puga (2002) fo some empiical suppot. 30

33 Acknowledgements Pat of this pape was witten when I was visiting the Magya Nemzeti Bank, I am gateful fo thei hospitality. I am also indebted to the CEU-Labo Poect whee mao data cleaning was caied. Fo comments and suggestions I thank László Halpen, Gianmaco Ottaviano, Péte Benczú, Fabice Defeve, Gábo Kézdi, István Kónya, and Álmos Telegdy as well as semina paticipants at Cental Euopean Univesity, Magya Nemzeti Bank, Wasaw Univesity, HWWA semina in Hambug and Sieps semina at the Stockholm Univesity. 31

34 Refeences Amiti, M. and Javocik, B. S. (2003), Tade costs and location of foeign fims in China. Wold Bank. Amiti, M. and Pissaides, C. A. (2001), Tade and industial location with heteogeneous labo, Technical epot, CEP/LSE. Baldwin, R., Foslid, R., Matin, P., Ottaviano, G. and Robet-Nicoud, F. (2003), Public Policy and Spatial Economics, MIT Pess. Baios, S., Stobl, E. and Gog, H. (2003), Multinationals.location choice, agglomeation economies and public incentives, Discussion Pape 17, CORE. Bay, F., Stobl, E. and Gog, H. (2003), Foeign diect investment, agglomeation and demonstation effects: An empiical investigation, Weltwitschaftliches Achiv (139), Bata, G. (2003), Developments in the geogaphy of hungaian manufactuing. In Munkaeõpiaci Tükö. Basile, R. (2004), The locationaacquisition vesus geenfield investments: the location of foeign manufactues in Italy, Regional Science and Uban Economics 34(1), Békés, G. and Muaközy, B. (2005), Fim behavio and municipal policy: The case of Hungay, Discussion Pape 05/04, MTA KTI. Boudie-Bensebaa, F. (2005), Agglomeation economies and location choice: Foeign diect investment in Hungay, Economics of Tansition 13(4), Bown, J. D., Eale, J. S. and Telegdy, Á. (2004), Does pivatization aise poductivity?, Discussion pape 25, KTI. Cameon, C. A. and Tivedi, P. K. (1998), Regession Analysis of Count Data, Cambidge Univesity Pess. Calton, D. W. (1983), The location and employment choices of new fims, Review of Economics and Statistics 65 ( ). Cieslik, A. (2003), Location deteminants of multinational fims within Poland. Wasaw Univesity. Cozet, M., Maye, T. and Mucchielli, J.-L. (2004), How do fims agglomeate? A study of FDI in Fance, Regional Science and Uban Economics 34(1), Disdie, A.-C. and Maye, T. (2004), How diffeent is Easten Euope? Stuctue and deteminants of location choices by Fench fims in Easten and Westen Euope, Jounal of Compaative Economics 32(2), Dixit, A. and Stiglitz, J. E. (1977), Monopolistic competition and optimum poduct divesity, Ameican Economic Review 67, Fazekas, K. (2003), Effects of foeign diect investment on the pefomance of local labo makets the case of Hungay, Budapest Woking Papes on the Labou Maket No 03/03, KTI. Figueiedo, O., Guimaaes, P. and Woodwad, D. (2002a), Home-field advantage: location decisions of potuguese entepeneus, Jounal of Uban Economics 52(2), Figueiedo, O., Guimaaes, P. and Woodwad, D. (2002b), Modeling industial location decision in US counties. Univesity of South Califonia. 32

35 Refeences Figueiedo, O., Guimaaes, P. and Woodwad, D. (2004), A tactable appoach to the fim location decision poblem, Review of Economic and Statistics. Fuita, M., Kugman, P. and Venables, A. J. (1999), The Spatial Economy: Cities, Regions and Intenational Tade, MIT Pess, Cambidge. Hais, C. (1954), The maket as a facto in the localization of industy in the United States, Annals of the Association of Ameican Geogaphes 64 ( ). Hausman, J. and McFadden, D. (1984), A specification test fo the multinomial logit model, Econometica 52, Head, K. and Maye, T. (2004), The empiics of agglomeation and tade, in V. Hendeson and J. F. Thisse (eds), Handbook of Regional and Uban Economics, Vol 4., numbe 15, Noth-Holland, Amstedam, dp 59. Head, K. and Maye, T. (2005), Maket potential and the location of Japanese fims in the Euopean Union, Review of Economics and Statitistics. Head, K. and Ries, J. (2001), Oveseas investments and fims expots, Review of Intenational Economics 9(1), Heiss, F. (2002), Stuctual choice analysis with nested logit, Stata Jounal 2(3), Holl, A. (2004), Manufactuing location and impacts of oad tanspot infastuctue:empiical evidence fom Spain, Regional Science and Uban Economics 34(3), Hummels, D. (2000), Towad a geogaphy of tade costs. Univesity of Chicago. Kugman, P. R. (1991), Inceasing etuns and economic geogaphy, Jounal of Political Economy 99, Kugman, P. and Venables, A. J. (1995), Globalization and the inequality of nations, Quately Jounal of Economics (110), KSH (2001), Ágazati kapcsolatok mélege 1998 (input-output tables), Technical epot, Központi Statisztikai Hivatal. Leame, E. and Stolpe, M. (2000), The economic geogaphy of the intenet age. UCLA. Maddala, G. S. (1983), Limited Dependent and Qualitiative Vaiables in Econometics. Cambidge Univesity Pess. Mashall, A. (1920), Pinciples of Economics. Macmillan Pess. McFadden, D. (1974), Conditional Logit Analysis of Qualititative Choice Behaviou. Academic Pess, New Yok, NY. Midelfat-Knavik, K. H., Oveman, H. G. and Venables, A. J. (2000), Compaative advantage and economic geogaphy: estimating the deteminants of industial location in the EU. LSE. Ottaviano, G., Tabuchi, T. and Thisse, J.-F. (2003), Agglomeation and tade evisited, Intenational Economic Review. Puga, D. (2002), Euopean egional policies in light of ecent location theoies, Jounal of Economic Geogaphy 2(4), Redding, S. and Venables, A. J. (2004), Economic geogaphy and intenational inequality, Jounal of Intenational Economics 62(1),

36 Magya Nemzeti Bank Smith, D. F. and Floida, R. (1994), Agglomeation and industial location: An econometic analysis of apanese-affiliated manufactuing establishments in automotive-elated industies, Jounal of Uban Economics 36, Telegdy, Á. (2004), Apeh dataset cleaning steps, Labo poect. Cental Euopean Univesity. Tain, K. (1986), Qualitative Choice Analysis: Theoy, Econometics, and an Application to Automobile Demand. MIT Pess, Cambidge, MA. Tain, K. (2003), Discete Choice methods with Simulation. Cambidge Univesity Pess, Cambidge. Wooldidge, J. M. (2002), Econometic Analysis of Coss Section and Panel data. MIT Pess. 34

37 7. Appendix 7.1. Fims vesus plants A key issue is the exact natue of fim location. In effect, plant level data would be necessay to epesenting the actual poduction site. Howeve, only fim level data ae available instead. As a esult, we may have data on a fim headquate, athe than its poduction plant distoting ou esults a geat deal. To check this, two execises wee caied out. Fist, the National Copoate Registe was consulted to see how lage foeign manufactues such as Siemens, Philips o IBM wee incopoated in Hungay. Appaently, these multinationals established sepaate entities fo many of thei opeations. Siemens AG, a Geman electonics good manufactue established a dozen fims up to 2003 including Siemens kft, esponsible fo all etail activities, Siemens Finance (Financial Sevices), o Siemens Telefongyá (Telecom). IBM has its main poduction plant as pat of IBM Data Stoage Systems in Székesfehévá (Feé county), while consulting business is caied out via IBM Üzleti Tanácsadó egisteed in Budapest downtown. The best example fo sepaation of plants by industies may be the Dutch giant, Philips. It has invested in vaious fims including Philips Components (machiney) in Gyõ (Gyõ-Sopon-Moson county), Philips Industies Hungay (electonics) in Székesfehévá (Feé county), Philips Monito Industies in Szombathely (Vas county) and Philips Hungay Sales in Budapest. A simila stuctue may be peceived by many othe mao multinational companies including Audi poduce Posche Inte Auto, o Electolux, whose poduction plant is situated somewhee in the countyside as one fim, while anothe one in Budapest is esponsible fo sales o foeign tade. One should expect that the most poblematic bias would come fom an ove-epesentation of the capital city given that many fims that enteed Hungay, fist established a HQ in Budapest. Thus, in a second effot, industy-level aggegates fom two souces wee compaed: The APEH complete fim level copoate dataset and plant level employe data of the Labou Maket Suveys. It showed that the shae of Budapest by industies is ust a few pecentage points highe in the fim level data. This also suppots the assumption that the application of fim level data should be of no geat concen in ou pactice Coections to the data Thee has been seious effot invested in cleaning the data and seveal coections wee made to the oiginal APEH dataset by the Magya Nemzeti Bank, the CEU Labo Poect 23 and the autho. Thee has been thee impotant steps. Fist, Longitudinal links fo foeign fims wee impoved using data povided by Hungaian statistics office KSH on copoate enty and exit. CEU Labo Poect looked fo othe longitudinal links in which the fims did not simply appea unde a new id numbe, but actually split up 23 Fo a basic desciption, see Bown et al. (2004) and fo details see Telegdy (2004). 35

38 Magya Nemzeti Bank into seveal fims o wee fomed via a mege. These allowed to keep tack most but not all of fims unde tansfomation. Second, The owneship stuctue of new fims was epaied in many cases to make sue that foeign owneship eflected the most likely case. Infomation fom balance sheets and adacent yeas values wee used. Thid, sales data fo all fims wee checked to avoid typing eos. Fo many fims, sales data wee missing. Futhe poblems I found and/o leaned fom othes woking with the same o simila datasets included: (1) 0 is imputed instead of actual figues fo sales, (2) thousands witten instead of millions, (3) one digit is left out making sales figue be 1/10 of actual data, (4) sales and expot sales figues swapped. Oveall, I made modifications eaching almost 2% of the total dataset. In some cases, sales could be estimated by using othe balance sheet figues, and in othes, the simple aveage of sales data at (t 1) and (t+1) was used. As a final note, emembe that fo some discete choice datasets, one has to woy about classification eo i.e. measuement eo in the left hand side vaiable. Having only a list of fim location decisions, the actual place may be mistyped o simply pooly gatheed. This is not the case with the APEH dataset, since tax epots ae submitted by the company to the egional Tax Authoity office, and thee ae one pe office pe county (except fo Budapest whee thee ae thee). As a esult, thee should be vey little eo in the choice of location vaiable. 36

39 Magya Nemzeti Bank Table 1 New foeign manufactuing fims pe Hungaian counties ( ) New fims New fims New fims Inhabitants Numbe of pe capita duing fist duing second Counties ( 000) new fims* ( 000) five yeas five yeas Szabolcs-Szatmá-Beeg Bosod-Abaú-Zemplén Békés Hadú-Biha Jász-Nagykun-Szolnok Somogy Tolna Nógád Heves Feé Csongád Bács-Kiskun Pest Veszpém Zala Baanya Komáom-Esztegom Vas Gyõ-Moson-Sopon Budapest city Souce: KSH, APEH Copoate dataset, autho s calculations. 38

40 Appendix Table 2a New foeign manufactuing fims by industies (NACE code) Industies All FDI Geenfield (17) Textile (18 & 19) Cloths, leathe (20 & 21) Pape and wood poducts (22) Pess (23 & 24) Refiney and chemicals (25) Plastic ubbe (26) Othe non-metalic (27) Metal -basic (28 Metal -fabicated (29 Machiney (30 Office equipment (31 Electic machines (32 & 33) Audio-video, PC, etc. instuments (34 & 35) Moto vehicles (36) Funitue, etc Total manufactuing (ex-food) Souce: APEH Copoate dataset, autho s calculations. Table 2b Aveage unit tanspotation costs by industy (NACE code) Industies Unit pice * (17) Textile 11.6 (18 & 19) Cloths, leathe 31.5 (20 & 21) Pape and wood poducts 5.8 (22) Pess 22 (23 & 24) Refiney and chemicals 18 (25) Plastic ubbe 12 (26) Othe non-metalic 8 (27) Metal -basic 6 (28 Metal -fabicated 31 (29 Machiney 27 (30 Office equipment 140 (31 Electic machines 45 (32 & 33) Audio-video, PC, etc. instuments 140 (34 & 35) Moto vehicles 41 (36) Funitue, etc. 10 Total manufactuing (ex-food) - Souce: Wold Bank, APEH Copoate dataset, autho s calculations. *Unit pice in USD/kg oiginal Wold Bank data in ISIC tems, unit pices wee tansfomed to NACE categoies and aggegated by the autho. 39

41 Magya Nemzeti Bank Table 3 Summay statistics Vaiable Desciption Souce Mean Std. Dev. IPC income pe capita (Ft, 000) KSH Pop population size ( 000) KSH IPloc own industy local output APEH, AKM of KSH Ipnat own industy national access APEH, AKM of KSH SAloc local supplie access APEH, AKM of KSH MAloc local maket access APEH, AKM of KSH SAnat national supplie access APEH, AKM of KSH MAnat national maket access APEH, AKM of KSH BAloc local business access APEH, AKM of KSH Tel_size Size of telephone netwok KSH (fixed line subscibes) Road_size Size of highway netwok (km) KSH Edu_size numbe of college students KSH Density population density: inhabitants/aea KSH dsouth Distance of Southen expot bode (km) HAS-Institute of Economics dwest Distance of Westen expot bode (km) HAS-Institute of Economics daipot Distance of Aipot (km) HAS-Institute of Economics Wage local wage (Ft) Minsity of Labo LMS Wage_ind local, own industy wage (Ft) Minsity of Labo LMS Wage_bc local blue-colla wage (Ft) Minsity of Labo LMS Wage_off local office wage (Ft) Minsity of Labo LMS Wage_man local manage wage (Ft) Minsity of Labo LMS Dl Road distance between cities (km) HAS-Institute of Economics KSH: Hungaian Cental Statistics Office, AKM : Input-output tables, LMS : Annual Labou Maket Suvey, APEH: Hungaian Tax Authoity s copoate database. NB All vaiables in estimations ae taken in logs. 40

42 Appendix Table 4 Conditional logit estimates Specification CL (1) CL (2) CL (3) CL (4) CL (5) CL (6) Fixed effects no no Region FE no no 7 7 no no Ln (income pe capita) 0.91*** (0.17) 0.03 (0.22) (0.27) (0.21) (0.38) 0.06 (0.35) Ln (population size) 0.20** (0.09) (0.34) (0.36) (0.36) 0.01 (1.22) 1.47*** (0.24) Ln(own industy local output) 0.22*** (0.01) 0.20*** (0.01) 0.21*** (0.02) 0.21*** (0.02) 0.21*** (0.02) 0.21*** (0.02) Ln (own industy national access (0.06) -0.19*** (0.06) -0.16** (0.07) -0.17** (0.07) -0.24*** (0.07) -0.26*** (0.07) Ln(local supplie access) 0.10** (0.04) 0.03 (0.04) 0.11** (0.05) 0.10* (0.05) 0.11** (0.05) 0.09 (0.06) Ln(local maket access) -0.17*** (0.03) -0.17*** (0.03) -0.10** (0.05) -0.12** (0.05) (0.05) -0.09* (0.05) Ln (national supplie access -0.35*** (0.12) -0.88*** (0.15) -0.62*** (0.15) -0.60*** (0.15) -0.99*** (0.17) -1.04*** (0.17) Ln (national maket access) 0.64*** (0.01) 0.41*** (0.12) 0.65*** (0.13) 0.60*** (0.13) 0.47*** (0.14) 0.40*** (0.14) Ln (local business access) 0.33*** (0.05) (0.07) (0.08) (0.08) -0.17* (0.10) (0.10) Ln (local wage) -0.82** (0.39) (0.45) 0.60 (0.52) 1.23** (0.63) Ln (local, own industy wage) -0.40** (0.19) -0.37* (0.19) Ln (local blue-colla wage) 0.42** (0.20) 0.37* (0.20) Ln (numbe of college students) 0.51 (0.28) 0.78** (0.32) 0.79** (0.32) Ln (size of highway netwok) 0.25** (0.11) 0.89* (0.51) 0.98* (0.51) (0.59) (0.69) Ln (size of telephone netwok) 0.18** (0.09) 0.25*** (0.10) 0.19** (0.09) (0.13) Ln (population density) 0.09 (0.07) (0.61) Ln (avg. distance expot bodes) -0.62*** (0.13) -0.53*** (0.14) -0.60*** (0.14) -1.40* (0.84) (0.44) Ln (distance to Aipot) -0.62*** (0.13) Ln (distance to Westen bode) -0.39*** (0.06) Ln (distance to Southen bode) -0.22*** (0.05) LR chi squaed Log likelihood McFadden's pseudo R squaed Numbe of obsevations Standad eos in paentheses * significant at 10%; ** significant at 5%; *** significant at 1%. 41

43 Magya Nemzeti Bank Table 5 Nested logit estimates Specification NL1 NL2 NL3 NL4 NL5 NL6 Top level altenatives FE NO YES NO YES YES NO Ln (income pe capita) 0.67*** (0.18) 0.13 (0.55) (0.31) (0.52) 0.45 (0.38) (0.35) Ln (population size) 0.32*** (0.13) 2.67 (1.90) 1.74*** (0.42) 2.35*** (0.59) 2.61*** (0.63) (0.58) Ln (local copoate access) 0.18*** (0.04) Ln (national copoate access) 0.24*** (0.04) Ln(own industy local output) 0.37*** (0.04) 0.36*** (0.03) 0.35*** (0.03) 0.36*** (0.03) 0.28*** (0.02) Ln (own industy national access -0.39*** (0.11) -0.30*** (0.10) -0.43*** (0.11) -0.48*** (0.11) -0.17** (0.07) Ln(local supplie access) 0.21** (0.09) 0.08 (0.08) 0.23*** (0.08) 0.19** (0.08) 0.09* (0.05) Ln(local maket access) (0.09) -0.18** (0.07) (0.08) (0.08) (0.04) Ln (national supplie access -1.51*** (0.27) -0.96*** (0.22) -1.45*** (0.26) -1.58*** (0.26) -0.74*** (0.16) Ln (national maket access) 0.46** (0.22) 0.84*** (0.19) 0.66*** (0.22) 0.60*** (0.22) 1.03*** (0.13) Ln (local business access) 0.29*** (0.19) 0.48*** (0.12) (0.18) -0.35* (0.18) (0.11) Ln (local wage) -1.34*** (0.45) 1.88** (0.84) 0.91 (0.69) Ln (local, own industy wage) (0.22) -0.42** (0.21) -0.38** (0.22) Ln (avg. distance expot bodes) 0.80 (1.75) -1.52*** (0.33) 0.19 (0.88) 0.96 (1.08) Ln (size of highway netwok) (0.96) 0.23* (0.11) Ln (numbe of college students) 1.38*** (0.52) Ln (Size of telephone netwok) (0.22) 0.21* (0.11) Inclusive value *** 1.99*** 1.96*** 1.90*** 1.46*** Inclusive value * 3.33*** 3.09*** 2.87*** *** Inclusive value ** 1.95*** 2.99*** 3.37** 1.89*** 2.28*** Inclusive value *** 1.87*** 3.53*** 2.11*** Inclusive value *** Inclusive value Inclusive value *** Method NLRUM NLRUM NLRUM NLRUM NLRUM NLRUM Numbe of obsevations Model LR chi Log likelihood LR test of IVs= (0.00) (0.00) (0.00) (0.00) 56.9 (0.00) (0.00) Standad eos in paentheses * significant at 10%; ** significant at 5%; *** significant at 1%. 42

44 Appendix Table 6 Coss coelation of vaiables Access vaiables Ln (own ind Ln (own ind Ln (local Ln (local Ln (nat Ln (nat loc acc) nat acc) supp acc) maket acc) supp acc) maket acc) Ln (own industy local output) 1.00 Ln (own industy national access Ln (local supplie access) Ln (local maket access) Ln (national supplie access Ln (national maket access) Development elated vaiables (including Budapest) ln (income Ln (bus. LN (local Ln (distance Ln (tele- Ln (highway pe capita) sevice acc) wage) bode) phone netw) netw) ln (income pe capita) 1.00 Ln (business sevice access) LN (local wage) Ln (weighted distance of expot bodes) Ln (size of telephone netwok) Ln (size of highway netwok) Development elated vaiables (excluding Budapest) ln (income Ln (bus. LN (local Ln (distance Ln (tele- Ln (highway pe capita) sevice acc) wage) bode) phone netw) netw) ln (income pe capita) 1.00 Ln (business sevice access) LN (local wage) Ln (weighted distance of expot bodes) Ln (size of telephone netwok) Ln (size of highway netwok)

45 Magya Nemzeti Bank Table 7 Genealised Hausman tests of IIA χ 2 test (p-value) 7 NUTS2 egions No county fixed effects With county fixed effects All vesus no Region *** (0.00) *** (0.00) All vesus no Region *** (0.00) 47.59*** (0.00) All vesus no Region *** (0.00) 69.68*** (0.00) All vesus no Region *** (0.00) 44.28** (0.01) All vesus no Region (0.161) (0.47) All vesus no Region *** (0.00) 51.68*** (0.00) All vesus no Region *** (0.00) (0.11) χ 2 test (p-value) 3 lage egions: West, East, Cental No county fixed effects With county fixed effects All vesus no West (0.00) (0.00) All vesus no Cental (0.00) (0.00) All vesus no East (0.00) (0.00) 44

46 Appendix Table 8 Location choice with count data egessions Specification CNT(1) CNT(2) CNT(3) CNT(4) CNT(5) CNT(6) Model Poisson Poisson Poisson Poisson NegBin NegBin FE No No County Aea, time No Aea, time Ln (income pe capita) 1.62*** (0.11) 0.65*** (0.15) (0.26) 0.39*** (0.12) 0.92*** (0.19) 0.53*** (0.16) Ln (population size) 0.82*** (0.07) Ln(own industy local output) 0.23*** (0.01) 0.24*** (0.01) 0.25*** (0.01) 0.25*** (0.01) 0.26*** (0.01) 0.26*** (0.01) Ln (own industy national access) -0.02* (0.01) -0.03** (0.01) -0.04*** (0.01) -0.03** (0.01) -0.12*** (0.02) -0.11*** (0.02) Ln(local supplie access) -0.09*** (0.02) -0.12*** (0.02) -0.07*** (0.02) -0.14*** (0.02) -0.17*** (0.03) -0.18*** (0.03) Ln(local maket access) -0.06*** (0.02) -0.08*** (0.02) 0.02 (0.02) (0.02) -0.05* (0.03) 0.03 (0.03) Ln (national supplie access) 0.02 (0.02) 0.07*** (0.02) 0.04 (0.03) 0.12*** (0.02) 0.25*** (0.04) 0.29*** (0.04) Ln (national maket access) 0.17*** (0.02) 0.08*** (0.02) 0.01 (0.03) 0.13*** (0.02) 0.05* (0.03) 0.08*** (0.03) Ln (local business access) (0.04) -0.10** (0.04) -0.13*** (0.03) 0.47*** (0.02) -0.12* (0.06) 0.43*** (0.03) Ln (local wage) -0.86*** (0.08) Ln (local, own industy wage) -0.68*** (0.06) -0.58*** (0.06) -0.81*** (0.07) -0.76*** (0.08) -0.86*** (0.09) Ln (local blue-colla wage) Ln (numbe of college students) 0.73*** (0.07) 0.63*** (0.09) Ln (size of highway netwok) 0.05 (0.04) 0.02 (0.06) Ln (population density: inhabitants/aea) 0.11*** (0.05) 0.13* (0.06) Ln (size of telepohone netwok) 0.11* (0.06) 0.18** (0.09) Ln (avg distance expot bodes) -0.48*** (0.05) -0.40*** (0.07) Ln Distance of Aipot Ln Distance of Westen expot bode Ln Distance of Southen expot bode LR χ Log likelihood McFadden s pseudo R Ove-dispesion α LR (α=0), χ01 (p-value) 833 (0.00) 730 (0.00) Numbe of obsevations Standad eos in paentheses. Significance at 1%, 5% and 10% is denoted by ***, **, and *, espectively + χ01: is a one-sided χ2 test of the ove-dispesion paamete, α. 45

47 Woking Papes 2005/7 Location of manufactuing FDI in Hungay: How impotant ae inte-company elationships? Pint: D-Plus H 1033 Budapest, Szentendei út

48