Determinants of FDI for Spanish regions:Evidence using stock data.∗

Laura Montolio a†, Mariam Camarero ‡and Cecilio Tamarit §

July 26, 2017

Abstract

After two decades of academic debate on the factors that determine FDI, discussionstill remains open. This article empirically investigates the determinants of FDI activityfrom a macroeconomic point of view, using Spanish regional data (NUTS 2) for theperiod 2004-2013. We apply the Poisson Pseudo-Maximum-Likelihood estimator withcountry-time fixed effects in a gravity framework. The empirical analysis revealed thefollowing allocation patterns: FDI locational strategies in the Spanish regions are de-termined significantly by gravity factors related to market conditions (such as marketsize and labour productivity), the geographic position of the regions and by trade lib-eralization policies. We also confirm the negative impact of the global financial crisison the stock of FDI and an improved specification of the model compared to previ-ous empirical literature based on FDI flows. The results allow us to draw some policyimplications about the prospective promotion of incoming FDI at the subnational level.

Keywords: Foreign Direct Investment determinants; PPML; Gravity model; Spanish re-gionsJEL classification: F21, R12, C23

∗The authors are grateful to Matilde Mas Ivars for kindly providing data of the Valencian Institute of Eco-nomic Research (Ivie). We are also indebted to Isaac Barbero and Emilio Carmona from the Spanish Ministryof Economy, for providing us with the FDI stock data from the DATAINVEX database. Furthermore, we wouldlike to thank Jeffrey Bergstrand, Estrella Gómez, Rafael Llorca and María Rochina for kindly providing advicewith technical doubts. We also acknowledge the financial support from the Spanish Ministry of Economy andCompetitiveness (BES-2015-072395 and ECO2014-58991-C3-2-R projects). All remaining errors are ours.†aCorresponding author: University of València and INTECO, Department of Applied Economics II, Av.

dels Tarongers, s/n Eastern Department Building E-46022 Valencia, Spain. Tel.: +34 963828922 , e-mail:Laura.Montolio@uv.es. ORCID: 0000-0003-0386-910X‡University Jaume I and INTECO, Department of Economics, Campus de Riu Sec, E-12080 Castellón (Spain).

ORCID: 0000-0003-4525-5181§University of València and INTECO, Department of Applied Economics II, Av. dels Tarongers, s/n Eastern

Department Building E-46022 Valencia, Spain. ORCID: 0000-0002-0538-9882

1. Introduction and motivation

Foreign Direct Investment (FDI) has become the engine of the globalization process. It is

broadly accepted that this type of investment has beneficial effects in terms of job creation

and technological transfers. Therefore, most governments in the world, either at a national

or regional level, have promoted generous policies to attract FDI on their soil. However,

there is no current consensus in the literature about the drivers of FDI and, therefore,

the most effective policies to promote it. In the European case, the significant increase in

FDI flows after the launching of the euro gave rise to increasing and persistent external

imbalances1, and examining the factors that attract direct investment has become a "hot

topic" in the rebalancing debate in the Eurozone, that is far from being solved empirically.

Thus, there is a growing body of research that provides empirical evidence not only on

the factors that determine FDI, but also about the existence of significant disparities in its

distribution across countries.

Indeed, the peripheral European countries became huge capital importers until the

outbreak of the crisis and Spain was by far the largest capital importer in absolute terms.

Figure 1 presents the inward FDI stocks 2 and inflows to Spain during the period 1970-2014.

Until the mid−eighties FDI was negligibly small. Since then, FDI inflows began to increase

after the Spanish entry in the EU in 1986 and later with the creation of the monetary union.

In particular, FDI inflows have increased from US$ 4.570, 7 million in 1987 to US$ 76.992, 5

million in 2008. However, the impressive upward trend in FDI inflows was disrupted by

the global financial crisis in 2008. Since 2008, FDI inflows have declined. However, the

bar graph in Figure 1 shows that although the FDI inward stock has slightly declined, it

remains large and suggests that Spain is still an attractive host country for FDI.

Although there is an extensive empirical literature at the country-level for developed

countries 3, the evidence about the determinants of FDI at the regional level is quite scarce

(Kandogan (2012) and Chan et al. (2014)). This scarcity mainly derives from the lack of

data. Some efforts have been made, however to collect and streamline data for regions by

1According to EEAG (2014), there were two primary and interrelated causes explaining these imbalances:First, the monetary union created optimistic expectations regarding the rapid convergence of the peripheralcountries with the core ones in the Eurozone. Second, the introduction of the euro eliminated the exchange-rate risk and induced investors to disregard country-specific bankruptcy risks. Both causes generated aninvestment and credit boom in the periphery and implied a large-scale reallocation of capital from the core tothe periphery that materialized as current-account deficits.

2Note that data on inward FDI stock is only available since 1980.3Mainly OECD countries, since traditionally they have represented a prominent share of world’s FDI flows

(Bénassy-Quéré et al. (2007) and Talamo (2007)).

1

regional statistical offices. Studies reveal regional disparities in the distribution of FDI and,

in this regard, the case of Spain clearly arises as a prominent example.

In this context, examining the factors that attract FDI in Spain seems pertinent. Indeed,

the analysis of FDI determinants at the regional level within a country may be of special

interest for countries with a federal structure where regional authorities have a key role

in designing policies to encourage FDI. Moreover, the motivation for studying FDI from a

regional standpoint comes from the fact that a lot of interesting characteristics are hidden

at more aggregate levels. More importantly, FDI determinants and effects may be localized

and, thus, a regional analysis may be more appropriate to obtain better-grounded results.

This paper tries to contribute to the existing literature by providing further insights

on the determinants of FDI activity from a macroeconomic point of view, using Spanish

regional data for the period 2004-2013, and using Poisson Pseudo-Maximum Likelihood

(PPML) estimator in a gravity framework. Our aim is to shed some light on the increas-

ing and heterogeneous patterns of inward FDI that these regions have attracted since the

launching of the euro.

The FDI data used in this paper has been extracted from DataInvex, a rich and under-

exploited database that provides FDI data on FDI stocks and flows broken down by au-

tonomous communities or regions (NUTS 2) for the Spanish economy.

We rely on PPML estimator which produces unbiased and consistent estimates when

the dependent variable comprises of a large proportion of zero observations as in the case

of the present study.

Furthermore, we use inward FDI stock data, as an alternative to flows, because stocks

are much less volatile than flows. This is a salient feature of our study as our hypothesis

is that this variable is the most suitable for empirical studies, as Wacker (2013) has recently

corroborated. Flows are more volatile not only due to the existence of economic shocks,

but are also very dependent on individual large-scale investment decisions. For the sake

of comparison, we replicate the analysis using inward FDI flows for a longer data span

(1995-2013).

A complete understanding of the factors that determine FDI seems pertinent, given

the interest of many countries facing financial constraints to attract FDI in order to foster

economic activity. In this context, FDI stocks, as an alternative to flows, provide a better

approximation to the long-run behavior of investment decisions, the ones really relevant

for the generation of growth.

2

The reminder of the paper is organized as follows. Section 2 reviews the theoreti-

cal approaches and reports the main determinants of FDI, whereas section 3 presents the

empirical literature. Section 4 provides a description of the variables and the data sources.

Section 5 describes the models to be estimated together with the results and, finally, Section

6 concludes.

2. Theoretical background

From a theoretical point of view, the so-called eclectic or OLI paradigm put forward by

Dunning (1977, 1979) has been considered the standard workhorse theoretical framework

within this strand of literature. Dunning suggests that three types of advantages influence

the foreign investment decision of a multinational enterprise (MNE): Ownership, Location

and Internalization (OLI). Ownership advantages concern the importance of a firm owning

firm-specific assets, both tangible and intangible, such as pioneering technology, specific

know-how and management skills, which provide MNEs with essential advantages over

local firms. Locational advantages refer to all those factors a specific location owns that

make it more attractive for foreign investment. Finally, Internalization advantages refer to

those kinds of advantages that make more profitable for a firm to carry out transactions

internally (i.e.through a wholly-owned subsidiary) rather than through licensing or joint-

venture agreements.

A combination of ownership advantages, locational and internalization characteristics

appear to be significant FDI determinants in many empirical studies. Focusing on loca-

tional advantages, Dunning (2000) identifies four main motives that encourage MNEs to

engage in foreign production: market seeking, resource seeking, efficiency seeking and

strategic assets seeking. Market seeking motives correspond to FDI that aims at supplying

the local market or markets in adjacent territories. The host market size, its per capita

income and consumer demand (all of them to take advantage of the economies of scale)

are the main reasons behind market seeking FDI. Resource seeking companies are those in-

vesting abroad in order to obtain cheap natural resources and/or unskilled labour. Hence,

locational decisions depend on factor endowments differences. Efficiency seeking invest-

ment is designed to promote a more efficient division of labor or specialization of assets by

MNEs. Finally, strategic asset seeking FDI searches for resources such as technology, skilled

workers and assets that can support development of a firm’s worldwide and weaken the

competitive position of its competitors.

3

With the incorporation of multinational firms into the general equilibrium trade mod-

els from mid 1980s onwards, it became possible to base empirical work on theoretical

predictions regarding the relationship between MNE activity and home and host countries

characteristics Barba Navaretti and Venables (2004). The proximity vs. scale framework for

analyzing FDI is based on the new trade theory and allowed to explain the existence of

two basic types of FDI, namely horizontal (market-oriented) and vertical (export-oriented)

FDI. The first type of FDI is explained using the proximity-concentration hypothesis which

explains the trade-off between maximizing proximity to customers and concentrating pro-

duction to achieve scale economies (Horstmann and Markusen (1987)). In this regard,

horizontal FDI (or HFDI) may imply duplication of the entire production process in sev-

eral countries. In contrast, the second type (or VFDI) is explained by using the factor-

proportions hypothesis which accounts for the existence of vertically integrated firms with

geographically fragmented production. Therefore, vertical FDI implies only a part of the

firm’s organisation is implanted in the foreign market (see Faeth (2009)). In terms of HFDI,

the most important factor to attract FDI is the size and growth of the host country whereas

VFDI mainly looks for cost competitiveness. VFDI is conducted in order to minimize pro-

duction costs in the host country and then to export the output produced to the home

country or to third countries. Hence, the most important location factor for VFDI is re-

source endowment. Helpman (1984, 1985) showed that countries’ differences in relative

factor endowments (the so-called factor-proportions hypothesis) explained VFDI.

Combining vertical and horizontal motivations for FDI, Ethier and Markusen (1996)

and Markusen and Maskus (2002) formulated the knowledge-capital model. Markusen and

Venables (1998) explain the knowledge-capital model through two tradeoffs. The first and

key tradeoff is between the scale economy gains that come from dividing up production

and spatially dispersing it to be near customer concentrations. The second trade-off con-

cerns productive factors. The so-called VFDI strives to place each stage of production in the

nation where it is cheapest. However, it is important to make some remarks at this point.

It can be misleading to think of the former literature as a proper FDI theory. In fact, it is a

Foreign Affiliate Sales (FAS) literature as qualified by Markusen in his 2002 book 4. More

recent contributions to this theoretical approach allow for heterogeneous firms. Helpman

et al. (2003) consider firms that have different productivities and fixed costs of establishing

"beachhead" in various markets. The most competitive firms tend to sell much more and

4Markusen and Maskus (2002)

4

thus tend to find the transport-cost-saving aspect of FDI especially attractive. This would

explain the widely spread phenomenon that FDI is dominated by large firms. Carr et al.

(2016) provided the first empirical test of the knowledge-capital model’s hypotheses. Us-

ing a panel of data (US outbound and inbound affiliate sales in many nations from 1986 to

1994), they find evidence for both the horizontal and vertical motivations for FDI. Blonigen

et al. (2003) question their econometrics, which, when corrected, no longer supports the

vertical motivations for MNE activity. Overall, under the knowledge-capital model, simi-

larities in market size, factor endowments and transport costs were determinants of HFDI,

while differences in relative factor endowments determined VFDI. The knowledge-capital

model has recently been extended to explain other forms of FDI such as export-platform

FDI (see Ekholm et al. (2007); Bergstrand and Egger (2004)) which is used to serve the

neighboring markets of the host country. To sum up, while recent Eaton-Kortum (Ri-

cardian) type models have been extended to motivate gravity equations for multinational

production (MP) of firms either in isolation from trade flows (Ramondo (2014)) or with

trade flows (Ramondo and Rodríguez-Clare (2013)), theoretical foundations for FDI per se

are limited primarily to Bergstrand and Egger (2007a).5

Indeed, a third strand of the theoretical literature on FDI determinants is the one based

on the gravity approach to FDI. Theoretical foundations for FDI are limited basically to

Bergstrand and Egger (2007a) and Head and Ries (2008a). These papers provide general

equilibrium theories for FDI and not FAS 6. In this case as in gravity models applied to

trade flows, the main explanatory variables are geographical location (due to its effects on

transport costs) and the size of the country, crucial when economies of scale are recognized

to exist in the activities of MNEs. The gravity model states that the closer two countries

are (geographically, economically and culturally) the higher will be the FDI activity be-

tween them. Recent papers have provided some micro foundations for the gravity FDI

specification. Head and Ries (2008b) develop a model of cross-border M&A activity where

a mother company has a randomly assigned advantage in controlling the company in the

host country, but faces a disadvantage in monitoring technology that gets more severe with

geographical distance. Bergstrand and Egger (2007a) add internationally mobile capital to

the “knowledge-capital" model and find that a “modified" gravity model fits the data bet-

5While Markusen and Maskus (2002) knowledge-capital model is about FAS, Bergstrand and Egger (2007a)is about both, FAS and proper FDI.

6Recently, theory has been directed, as well as empirical work, to FAS, starting with Brainard (1997) andcontinuing to Ramondo et al. (2015)

5

ter. Similarly to Head-Ries, the Bergstrand-Egger model stresses the importance of relative

distance. In this case, the amount of expected FDI between two nations depends upon

the bilateral distance relative to some measure of the host and home nation’s distance to

alternative FDI sources and destinations.

Furthermore, changes in interest rates and exchange rates have been identified as ad-

ditional FDI determinants. This behavior of MNEs is explained by the risk diversification

hypothesis in which firms are seen as risk averse and trying to diversify business risk.

Lastly, it is important to consider also in this analysis of the theoretical models the

influence of political variables on FDI. The strategies adopted by companies and their

performance on international markets are largely determined by institutions (i.e. “the rules

of the game" as in Busse and Hefeker (2007)). In this context, foreign investment can be

regarded as a “game" in which the players are the multinational firm and the government

of the host country, or as a contest between governments to attract FDI (Faeth (2009)).

Policy variables such as corporate tax rates, tax concessions, tariffs and other fiscal and

financial investment incentives have thus been posited to have an effect on FDI.

Briefly stated, the literature suggests a variety of theoretical models explaining FDI that

do not necessarily replace each other (see Blonigen (2005), Faeth (2009) and Assunçao et al.

(2011) for a thorough literature review on FDI determinants). Therefore, in the next Section

we review the wide range of factors that can be considered in empirical studies in order to

find the determinants of FDI. These factors involve both micro (e.g., organizational aspects)

and macro (e.g., resource allocation) dimensions.

3. Survey of the empirical literature

The location determinants of FDI has been largely explored in the empirical literature.

Blonigen and Piger (2014) make an extensive overview of the empirical determinants of

FDI literature. As we have already stressed before, although sometimes difficult, it is

important to distinguish between proper FDI and FSA, being the latter derived from the

Multinational Production (MP) theory. 7. Even if in this review we may not always make

this distinction explicit, we are aware of its relevance and use it in the selection of the

econometric specification in Section 5. Results, however, may differ substantially from

country to country. Hence, there is a need for detailed country case studies, as they can be

7Paniagua (2011) extends the Kleinert and Toubal (2010) paper on gravity foundations for MP to differentiateFDI from MP

6

very informative. Since this study aims to identify the factors that have been found to best

explain inward FDI to a particular region, it concentrates on the macro dimension.

In order to set up a taxonomy of the empirical studies, we will distinguish between

those that focus on specific areas or country-groups (OECD, EU...), from those that rely

on country-specific analyses. The latter are developed both at national and regional levels.

It is worth mentioning that studies carried out at the national level have been the most

prolific. Some of these studies and their conclusions will be revisited below. First, we will

present the main studies that analyze FDI determinants for groups of countries; second, we

will report the most significant studies at the country level; then we will show an overview

of the main results for regional studies and finally, we will review the literature for the

Spanish case.

The lion’s share of the literature dealing with the determinants among groups of coun-

tries focuses on the OECD since traditionally they have been representing an outstanding

share of the world FDI inflows. Agiomirgianakis et al. (2003) is one of the first studies

that examined OECD countries. Using panel data techniques for the 1975-1997 period,

he provided robust evidence of the positive impact of human capital, trade openness and

infrastructure density on FDI inflows.

Within this group of studies it is worth mentioning Bénassy-Quéré et al. (2007). They

estimate a gravity equation using cross-section and panel data regressions for a set of 52

OECD countries. Unlike the vast majority of the literature on this field, they use bilateral

FDI stock as their dependent variable. The authors argue that there are several advantages

in working on stocks rather than flows. First, foreign investors decide on the worldwide

allocation of output, hence on capital stocks. Second, stocks account for FDI being financed

through local capital markets, hence it is a better measure of capital ownership (Devereux

and Griffith, 2002). Finally, stocks are much less volatile than flows which are sometimes

dependent on one or two large takeovers, especially in relatively small countries 8. In ad-

dition, they find that institutions are important determinants of inward FDI. These results

are also confirmed by Talamo (2007), who emphasized the positive effects of institutional

variables on FDI flows for OECD and developing countries.

More recent research by Economou et al. (2016) using both static and dynamic panel

techniques over the period 1980-2012 has identified lagged FDI, market size, gross capital

formation and corporate taxation as robust FDI determinants for OECD countries. More-

8Bénassy-Quéré et al. (2007), p.769).

7

over, Bruno et al. (2016) conduct OLS, Poisson and Heckman models on a gravity frame-

work for 34 OECD countries over 1985-2013. They conclude that GDP, GDP per capita and

EU membership affect positively FDI inflows. Although they focus on modelling flows,

they also replicate the estimations for FDI stocks and find qualitatively similar results.

Other country-group studies have focused on European Union countries and, in par-

ticular Central and Eastern European Countries, (CEEC) within the OECD. They try to

disentangle the specific importance of this economic integration agreement on FDI. Be-

van and Estrin (2004) use panel data techniques in a gravity framework to examine the

determinants of FDI in CEECs for the period 1994-2000. They identify unit labor costs,

gravity factors, market size and proximity as the most important drivers of FDI. Besides,

announcements about EU accession proposals have been found to have an impact on FDI

for the future member countries.

Similarly, Demekas et al. (2007) use both FDI flows and stocks to explain inward FDI

in 16 European transition economies, applying cross-section as well as dynamic panel data

techniques. In addition to gravity factors, they control for host country policy variables as

FDI determinants. They find that gravity factors, trade and foreign exchanges liberalization

as well as infrastructure reforms encourage FDI, while high unit labor costs, corporate taxes

and import tariffs discourage it.

For the case of EU countries, Canton and Solera (2016) estimates a Heckman two-step

selection procedure in the context of a gravity model for the period 2003-2014. His findings

suggest that business climate and product market regulations play a key role on attracting

greenfield investment in the EU.

There is a second strand of the empirical literature that analyzes case studies at country

level, both for developed and emerging economies. Boateng et al. (2015) investigate the

impact of macroeconomic factors on FDI inflows in Norway, focusing on locational ad-

vantages. They apply Fully Modified OLS (FMOLS) and vector autoregressive and error

correction model (VAR/VECM) on quarterly data. They find that real GDP, sectoral GDP,

the exchange rate and trade openness have a positive and significant impact on FDI in-

flows. Ang (2008) examines the determinants of FDI inflows for Malaysia, using annual

data for the period 1960-2005 and applying an error correction model. In this case, the

factors that attract FDI are real GDP, GDP growth, financial development, infrastructure

and trade openness. Focusing on Sri Lanka,Ravinthirakumaran et al. (2015) investigate FDI

inflows determinants for the period 1978-2013 and apply unit roots and cointegration tech-

8

niques (error correction model). Market size, trade openness and the level of infrastructure

are found to have a positive impact on FDI inflows.

Far less research has been carried out concerning the factors that attract FDI within a

particular country, that is, at a regional level. Moreover, most of these studies at the subna-

tional level have been conducted in large countries (the better part of them in China, but

also other countries such as the US), and only rarely in smaller countries. Using provincial

panel data from China, Chan et al. (2014) examine all possible flows of causality involving

FDI and a set of potential determinants, both in the short and in the long run. In a context

of error correction models with Granger causality tests they show that in both the short

and the long run, GDP growth directly influences FDI, while growth in local infrastructure

and local investment have indirect but not direct influence.

In the case of the US, Kandogan (2012) investigates within-country regional locational

decisions of multinationals. He uses states within the US as regions and applies multiple

regression analysis using both FDI stock and FDI employment as dependent variables.

They identify unemployment rate, market size, per capita income, income growth and

state regulations as determinants of FDI.

Although to a lesser extent, regional studies conducted within small countries are also

found in the empirical literature. This is the case of Chidlow et al. (2009), that using a

multinomial logit model investigate the location determinants of FDI inflows in Poland,

at a regional level. The authors claim that knowledge-seeking factors alongside market

and agglomeration factors act as the main drivers for FDI inflows into the Mazowieckie

region, while efficiency and geographical factors encourage FDI to other areas of Poland.

Similarly, Dimitropoulou et al. (2013) analyze FDI projects in UK regions to identify the

main determinants of the location choices of these investments. Using multinomial and

conditional logit models, they find that existing regional specialization is the single most

important determinant of inward FDI. Focusing on Croatian regions, Kersan-Skabic and

Tijanic (2014) apply static (random effects estimator) and dynamic panel data methodolo-

gies to analyze their attractiveness as FDI locations. They conclude that FDI inward stocks

are attracted to Croatia by education, infrastructure, the manufacturing industry and the

capital city region, while unemployment and EU-border regions have a negative effect on

FDI.

In the case of Spain, although most of the empirical evidence has been obtained at the

9

national level 9, since the early 1990’s the regional perspective has also been brought to the

forefront of this field of research. Egea Román and López Pueyo (1991) conduct a cluster

analysis for the period 1985-1989 and conclude that per capita and per employee income,

human capital and the productive structure are the main drivers of FDI. In contrast, the

unemployment rate, infrastructure endowment and subsidies are not found significant.

Pelegrín (2002), using three different methodologies (OLS, LS with fixed effects and GLS),

estimate a FDI equation for the period 1993-1998. Three key determinants of FDI location

are identified, namely, market size, human capital and public incentives; however, infras-

tructures are not significant while labour costs have a positive coefficient. In another paper,

Pelegrín and Bolancé (2008) finds that agglomeration economies and the concentration of

R&D activities are important drivers for manufacturing FDI in a model estimated using

GLS. Using the same methodology together with instrumental variables, Rodríguez and

Pallas (2008) show that real GDP, human capital, sectoral export potential and the differ-

ential between labor productivity and the cost of labor are key factors in explaining the

regional distribution of FDI during the period 1993-2002.

More recently, Villaverde and Maza (2012) analyze the regional distribution of FDI in

Spain and its main determinants between 1995 and 2005/2008 by means of panel data

techniques, namely by GLS and two stage GLS. The econometric analysis reveals that fac-

tors such as economic potential, labour conditions and competitiveness are important for

attracting FDI both at aggregate and sectoral levels.

Finally, Gutiérrez-Portilla et al. (2016) estimate a FDI equation by GMM and GLS over

the period 1997-2013, and show that FDI inflows in Spain are mainly determined by market

size, the level of human capital in interaction with wages, and the characteristics of Madrid

as capital of the nation. The research is conducted not only for the whole period and total

FDI but also for two sub-periods (pre-crisis and crisis) and areas of origin (Europe and

America).

Despite the amount of empirical work that has analyzed the factors that determine FDI,

the majority has focused on the evolution of flows. To the best of our knowledge, there are

no empirical papers dealing with regional FDI determinants based on stock data. Overall,

from the above literature review it can be inferred that the main FDI determinants in

Spain are those related to market-seeking (i.e.market size) and resource-seeking (i.e.human

capital, labor market conditions and physical infrastructure endowment). In the next two

9some examples of these studies are Bajo-Rubio and Sosvilla-rivero (1994), Pelegrín (2003) and Martínez-Martín (2011), among others

10

sections, adopting a regional perspective, we shed more light on this statement using FDI

stock data. The existence of an updated and largely unexploited database developed by

the Spanish Ministry of Economy (DataInvex) calls for further research in this field.

4. Data and stylized facts

In this section, and prior to present the empirical results, we describe the variables we

have chosen for the analysis based on the previous literature discussion, both from a the-

oretical and empirical point of view. In addition, we describe their sources and point to

some stylized facts. More detailed variable definitions and data sources can be found in

Table 2, while Tables 3 and 4 report some basic descriptive statistics and the correlations,

respectively. All the variables are in logarithms.

The dependent variable is bilateral inward FDI stock from the origin countries towards

the Spanish regions for the period 2004-201310. They have been obtained from DataInvex

from the Spanish Ministry of Economy, Industry and Competitiveness that provides data

on FDI disaggregated by regions. The dependent variable is FDI inward stocks instead of

flows because the former are much less volatile than the latter. This volatility has its origin

not only in the existence of economic shocks, but also on individual large-scale investment

decisions. However, we have also applied the analysis to inward flows (that are available

from 1995) for the sake of comparison. Furthermore, FDI stocks (as a valuation of the

cumulative FDI) provide a better approximation to the long-run behavior of investment

decisions, the ones really relevant to capture growth and the dynamic effects of economic

integration. In the same vein, Baldwin et al. (2008) argue that factors such as stock market

fluctuations or exchange rate volatility cause short-run variability on FDI flows that may

not always be linked to the explanatory variables and therefore lead to worse model fit for

flows than for stocks.

Our data set is annual and covers the period 2004-2013. Hence, we have a balanced

panel with dimension N=136 (17 regions x 8 countries, that is, all possible bilateral relation-

ships) and T=10. Although there is information available for a larger group of countries,

we have chosen to include just the eight largest investors, as they represent an average of

84% of total FDI during the sample period (see Figure 3). The number of observations is

10We do not include FDI information of ETVE (Empresas de Tenencia de Valores Extranjeros-brokers) firmsbecause they are considered instrumental companies whose existence obeys to fiscal optimization strategieswithin a business group and in many cases their investments lack direct economic effects.

11

NxT=1360. Table 1 reports the countries included in the study. We assume zero FDI in case

of non-reported data as in Canton and Solera (2016) and treat negative values of FDI stock

as zero following Gouel et al. (2012). As the latter point out, negative stock values have not

real economic meaning but are generally the consequence of accounting methods.

In search of the main determinants of inward FDI in the Spanish regions, we have

chosen a set of explanatory variables that capture the main factors likely to attract FDI,

considering not only the theoretical models but also the empirical studies previously dis-

cussed. These variables include factors describing labour market characteristics, the degree

of openness, as well as capital endowment, both physical and human11. The variables

considered are the following:

Market size (MS): proxied by GDP per capita. Most of the literature considers this

variable a robust determinant of FDI. From Dunning’s OLI framework, to the HFDI theory

(i.e. the proximity-concentration hypothesis) as well as the Knowledge Capital Model and

the gravity approach, all argue that the market characteristics of recipient countries or

regions such as size greatly influence the decision of a MNE to invest. As market’s size

increases, so do the prospects of higher demand (greater purchasing power), better market

opportunities for the firms and potentially higher returns on their capital. In this regard,

market-seeking are among the main motives for investors to undertake FDI. Larger markets

in the host region do not only denote good economic performance, but also allow for a

reduction in the cost of entry through the exploitation of economies of scale. Hence, we

would expect to find a positive relationship between market size and FDI.

Labour productivity (LP): defined as GDP per total employment, the role of this variable

would be also in line with the OLI framework, HFDI and the Knowledge Capital Model.

However, its effect on FDI could be either positive or negative. If the number of workers

is relatively scarce, labour productivity would be low and hence, the marginal return to

capital would be relatively high and FDI would become attractive. In such a context, we

would expect a negative effect on FDI. However, should labour productivity reflect factors

that attract investment, such as market size and the institutional conditions to operate

businesses, a positive relationship would be expected.

Human capital (HC): proxied by the share of population with tertiary education. Also ac-

cords with the OLI framework and the HFDI theory. All other things being equal, regions

with highly-skilled workers would be expected to compete more favorably than others

11It should be noted that the choice of variables was somewhat restricted by the availability of disaggregatedSpanish data.

12

in attracting FDI. Indeed, qualified workers attract FDI oriented to industrial sectors with

higher demand and technology. Hence, higher human capital is expected to have a positive

effect on FDI. However, if FDI was oriented to activities with a very low value added (fol-

lowing VFDI theories), it would seek cheap and less qualified workers. Hence, a negative

association between skilled labour and FDI could also be expected.

Trade openness (OP): defined by the sum of bilateral exports plus imports over GDP.

According to the OLI framework, the HFDI and the Knowledge Capital Model, a reduction

of barriers to external trade and, in general, a business-friendly economic climate would

increase investment in general, thus attracting FDI as well. In addition, it is expected

that MNEs would invest in trade-partner markets with whom they are already familiar.

Numerous empirical studies suggest that trade (imports and exports) complements rather

than substitutes FDI. Much of FDI is export oriented and may also require the import of

complementary, intermediate and capital goods. In either case, the volume of trade is

enhanced and thus trade openness is generally expected to be a positive and significant

determinant of FDI.

Differences in Relative Factor Endowments (RFE): proxied by differences in per capita GDP

between the host and the source country. Following, Mitze et al. (2012), the variable takes

a minimum of zero for equal factor endowments in the two regions. According to the

Knowledge-Capital model, differences in relative factor endowments determine VFDI. Ver-

tical or export-oriented FDI is conducted in order to minimize production costs in the host

country and then to export the output produced to the home country or to third countries.

In this regard, the most important location factors are resource endowments.

Acces to water (LANDLOCKED): defined as a dummy variable that takes the value 1 for

landlocked regions and 0 otherwise. In line with the Gravity approach, access to water

reduces transport costs and hence, would be expected to influence FDI positively. Thus,

it could be considered a location advantage, according to the OLI framework, in the sense

that it represents access to infrastructures that facilitate economic activities.

Before we turn to the specification of the empirical model used throughout this paper,

we aim at highlighting some stylized facts concerning the pattern of inward FDI in Spain.

The evolution of Spanish inward FDI stocks and flows is shown in Figure 1. Two styl-

ized facts emerge from the observation of the data: first, there are two dates that mark the

take off of Spain as a host for FDI, the participation of the peseta in the European Monetary

System in 1989 and the beginning of the European Monetary Union. Exchange rate stability

13

has been crucial in the reduction of risk and, then, the increase of the general attractiveness

of the country as an investment destination. Second, the evolution of FDI stocks show a

steady increase. Even during the crisis, the value of the stock is maintained (at least in

nominal terms) and only decreases with some delay, in 2013-2014. Looking at Figure 2,

the third fact we can observe is that the regional distribution of FDI is highly concentrated.

The map shows total inward FDI in each of the 17 Spanish regions 12 during the period

of analysis. FDI seems to be attracted mainly to four regions over the period 2004-2013.

Of these, Madrid has been by far the largest recipient of inward FDI in Spain (almost 3500

million euros), followed by Catalonia (with 1048 millions), Asturias (304 millions) and the

Basque Country (237 millions). Extremadura has attracted the lowest amount of FDI (an

average of 6 millions). Another stylized fact, also related to the regional distribution of

FDI, has to do with the geography of FDI: with the exception of the region of Madrid, the

other regions that concentrate more FDI are in the coast. In addition, potential regional

spillovers may influence Aragon’s and Navarra’s FDI (due to their closeness to Catalonia

and Valencia in the case of the former and to the Basque Country in the case of the latter).

Finally, Figure 3 shows the main source-countries of FDI in Spain. The largest stock of

FDI has come from the US (2601 millions of euros, in average, what represents the 19% of

inward FDI stock in Spain), followed by France, the UK and Germany, that jointly represent

over 60% of the total. Together with Mexico, the Netherlands, Luxembourg, Switzerland

and Portugal they constitute the ten larger foreign investors in Spain. In this study, we

restrict the analysis to the eight largest investors (US, France, the UK, Germany, Mexico,

the Netherlands, Portugal and Sweden).

5. Econometric specification and estimation results

From a methodological point of view, our empirical strategy will be based on the gravity

approach to FDI as posited in Anderson and van Wincoop (2003). However, as our variable

of interest is FDI and not FAS, we focus on the developments made by Head and Ries

(2008a) and Bergstrand and Egger (2007b) which, to the best of our knowledge, are the

only two formal general equilibrium theories for FDI. This setting is enlarged using some

additional variables following Blonigen and Piger (2014). Moreover, as the variable we are

interested in is inward FDI, most of the relevant determinants should be related to the

destination countries or regions (Blonigen et al. (2007)).

12We do not account for Ceuta and Melilla.

14

Consider the nonlinear form of the Anderson and van Wincoop (2003) gravity model:

xij =yiyj

yw

(tij

PiPj

)1−σ

(1)

where tij = bijdρij. Taking logarithms, and adding an error term, εij, a linear standard

gravity equation is obtained:

lnXij = lnyi + lnyj + (1− σ)ρlndij + (1− σ)lnbij − (1− σ)lnPi − (1− σ)lnPj + lnεij (2)

The mean of lnεij depends on higher moments of εij, thus including its variance. If εij is

heteroskedastic, which is highly probable in practice, then the expected value of the error

term depends on one or more of the explanatory variables because it includes the variance

term.

Based on Jensen’s inequality, that states that E[ln(εij) 6= [lnE(εij)]], Silva and Tenreyro

(2006) argue that estimating a log-linearized equation like the one shown in Equation 2 by

Ordinary Least Square (OLS) results in bias. The conditional distribution of the dependent

variable is altered and estimation by OLS would produce misleading estimates, as the t-

values of the estimated coefficients cannot be trusted.

They have proposed instead a Poisson Pseudo-Maximum Likelihood (PPML) estimator

which deals with this problem and provides consistent estimates of the original nonlinear

model. The PPML estimator has a number of additional desirable properties. First, it

is consistent under the presence of fixed effects; this is an important issue for the gravity

approach since most theory-consistent models require the inclusion of fixed effects. Second,

the PPML estimator naturally includes observations for which the FDI value is zero.

Actually, data in many country-pairs involve zero investment flows. In particular, in this

study the proportion of zero inward FDI stock from the 8 OECD countries constitutes the

35, 59% of the total13. Ignoring this large number of zeros would lead to misleading results.

Model specifications like the one in Equation2 do not incorporate absolute zero flows since

the natural logarithm of zero is undefined and is consequently dropped. Therefore, we

rely on PPML estimator proposed by Silva and Tenreyro (2006) which takes the following

general form:

13This proportion of zero FDI stocks corresponds to the 2004-2013 period. For the 1995-2013 period theproportion of zero FDI inflows is 73%.

15

yi = exp[Xiβ]εi (3)

In this equation, yi is a dependent variable such that yi ≥ 0 and E[εi|xi]. Since we are

using a count variable as dependent, i.e. a variable that is discrete and non-negative, the

PPML method is an appropriate estimator with an increasing recognition in the empirical

literature. Therefore, we rely on this estimator in the present paper.

Following the above discussions, the PPML estimator in the context of the current study

would take the following form:

FDIijt = β0 + β1kXikt + β2Zijt + β3l Dl + ψjt + ψt + εijt

t = 1, ..., T, k = 1, ..., K, l = 1, ..., L(4)

Where FDIijt represents inward FDI stock (the valuation of the cumulative FDI) received

by region i from country j in any period t. Matrix Xikt denotes all k FDI macroeconomic

determinants specific to the region such as market size, labour productivity and human

capital and Zijt contains bilateral determinants such as trade openness and differences in

factor endowments. Dl stands for dummy variables such as Landlocked and Crisis.

We also include country-origin varying fixed effects ψjt to capture all those varying

fixed effects of the investors, as well as time fixed effects ψt to control for business cycle

effects over the sample period. εijt is an error term such that εijt ∼ N(0, σ2). Note that β1k

and β3l are two vectors of k and l coefficients, respectively, associated to the explanatory

region-specific variables and the dummies.

More formally, our empirical specification and the expected signs of the FDI determi-

nants are as follows:

FDIijt = f

MSit LPit HCit LANDLOCKEDit OPijt RFEijt

(+) (+/−) (+/−) (−) (+) (+/−)

(5)

As mentioned previously in Section 4, (MS) is expected to positively influence the deci-

sion of a MNE to invest. (LP) could have either a positive or negative impact on FDI. If the

number of workers is relatively scarce, labour productivity would be low and hence, the

marginal return to capital would be relatively high and FDI would become attractive. In

such a context, we would expect a negative effect on FDI. However, if labour productivity

16

reflects factors that attract investment, a positive relationship would be expected. Similarly,

(HC) could be either positively or negatively correlated with FDI. If FDI is oriented to in-

dustrial sectors with higher demand and technology, a positive sign would be expected.

However, if FDI is oriented to activities with a very low value added, it would seek cheap

and less qualified workers. Hence, we would expect a negative association between skilled

labour and FDI. (OP) is generally expected to be a positive determinant of FDI. The more

trade exists between two regions, the more FDI will take place between them. Thus, there

exist a complementary relationship between trade and FDI.

Not having access to water (LANDLOCKED) increases transport costs and hence, would

be expected to influence FDI negatively. The effect of differences in relative factor endow-

ments (RFE) on FDI could be either positive or negative. High differences in relative factor

endowments may be perceived by MNEs as a signal of poor market conditions and FDI will

not be attractive. In such a context, we would expect a negative sign. However, if the differ-

ences in factor endowments are seen as cost advantages by MNEs, a positive relationship

would be expected.

Taking into account the theoretical and empirical surveys of the literature as well as the

main stylized facts, the present study proposes the following testing hypotheses:

• For the explanatory variables:

H1: Market size positively influences the decision of a MNE to invest (that is, β11 >

0).

H2: The relationship between labour productivity and inward FDI will be positive,

so that β12 > 0.

H3: Qualified workers has a positive influence on inward FDI (β13 > 0).

H4: Trade liberalization policies positively influence inward FDI, so that β14 > 0.

H5: Differences in terms of relative factor endowments reduce inward FDI, that is,

β2 < 0.

H6: Access to water provides a reduction in transport costs and leads to greater

inward FDI, hence β31 > 0.

H7: The crisis has reduced inward FDI in the Spanish regions and β32 < 0.

• For the dependent variable:

H8: FDI stock data are more appropriate than flows in econometric FDI analysis.

17

Table 5 reports the results for the Poisson-Pseudo Maximum Likelihood estimator for

different model specifications. Column (1) presents the estimated coefficients for the base-

line model; columns (2) and (3) present an alternative augmented version of the basic

model in order to test for additional FDI determinants, and column (4) controls for the

effect of the recent financial crisis. The adjusted− R2 as an overall indicator for the model

"fit" shows that all of the models are quite close and explain a significant part of the total

variation in the FDI equation (around 86− 87%).

Taking a closer look at the estimated coefficients, most of the determinants of FDI

highlighted in the literature are significant and have the expected sign. Market size (MS) is

found to have the expected positive effect on FDI (in line with the OLI framework, HFDI

model and Knowledge Capital Model, but in contrast to the VFDI model), illustrating that

a growth in market size makes Spanish regions a more attractive place to invest. This result

confirms the findings obtained in other studies, such as Egea and López-Pueyo (1991b) and

Pelegrín (2002). As expected, the coefficient for labour productivity (LP) is also positive

and significant (in line with the OLI framework, HFDI model and the Knowledge Capital

Model). Hence, labor market conditions seem to be another factor that has an influence

early on in the investment decision and that encourages FDI. Similar results were also

found by Hoang and Goujon (2014) for Vietnamese provinces and Rodríguez and Pallas

(2008) for the case of Spain.

The unexpected negative and significant sign for human capital (HC) is in contrast to

the OLI framework and HFDI theoretical predictions. The negative sign indicates that FDI

is oriented to activities with a very low value added (following the VFDI model), hence

seeking cheap and less qualified workers.

This result has also been found in Gutiérrez Portilla (2013) who states that a plausible

explanation for this could be the low dispersion in human capital data across Spanish

regions.

Trade openness (OP) has the expected positive effect on inward FDI stock. Hence, trade

liberalization and, in general, the process of economic integration at the European level

and globalization at the world level, encourage FDI.

This result supports also the view of a complementary relationship between FDI and

trade as in Pantulu and Poon (2003) and Mitze et al. (2012). Numerous empirical studies

suggest that trade (imports and exports) complements rather than substitutes for FDI. They

argue that much of FDI is export-oriented and may also require the import of complemen-

18

tary, intermediate and capital goods. Therefore, volume of trade is enhanced and thus

trade openness is generally expected to be a positive and significant determinant of FDI.

As for the estimated coefficient of the dummy variable LANDLOCKED, this one is found

to be negative and statistically significant in line with previous evidence in the gravity

framework. Therefore, access to water appears to be an important driver for FDI attraction,

in the sense that it reduces transport costs. The negative sign can be interpreted as an

indication that ocean transportation is significantly cheaper.

Differences in factor endowments (RFE) present no significant effect on inward FDI

stock. This result is consistent with the findings of Mitze et al. (2012) in the case of German

regions.

Finally, the global financial crisis has the expected negative effect on inward FDI stocks,

as reflected in the aggregated data shown in Figure 1.

We replicate the estimations for FDI inflows for the sake of comparison. Results are

presented in Table 6. Even though we do not consider all the alternative models to be

strictly comparable owing to data availability (inflow FDI specifications are estimated over

1995-2013 (T=19)), the interpretation of the estimation results is in a similar vein. We found

labour market characteristics (i.e. market size (MS) and labour productivity (LP)) are pos-

itive and significant determinants of FDI while human capital (HC) and not having access

to water (LANDLOCKED) deter FDI inflows. Differences in factor endowments (RFE) have

no significant effect on FDI inflows. Conversely, the financial crisis has a positive and sig-

nificant sign. This positive sign may be capturing some individual large-scale investment

decisions instead of the cumulative effect of the financial crisis which is better captured by

the FDI stock data.

Besides, we are interested in assessing whether FDI has been redirected from Southern

Europe to the CEECs as well as the effect of the monetary union (ie. EMU). To this end,

we introduce a dummy variable that takes value 1 from 2004 (when the CEECs joined

the EU), in order to control for the effect of the EU enlargement, as well as a dummy

variable equal to 1 since the creation of the Eurozone in 1999. 14 Results show EMU

turns out to be positive and significant, suggesting the common currency has a positive

influence on inward FDI, while EU enlargement has a negative and significant effect on FDI

inflows. This last result would be consistent with the view that FDI has been redirected14Specifications including separately each of these dummy variables have also been estimated. Both, EU

enlargement and EMU, are found to have no significant effect on FDI inflows. Results are available uponrequest.

19

from Southern Europe to the CEECs.

Potential specification errors in the estimated models, both for the FDI stock and flow

specifications, are tested by means of Ramsey’s Reset test Ramsey (1969). This is essentially

a test for the correct specification of the conditional expectation, which is performed by

testing the significance of an additional regressor constructed as (x′b)2, where b denotes

the vector of estimated parameters (Silva and Tenreyro (2006)). The corresponding p-values

are reported at the bottom of tables 5 and 6, respectively.

In the specifications using FDI inflows, the test rejects the hypothesis that the coefficient

on the test variable is 0. This means that these models are inappropriate, in the sense that

the functional form is incorrect. In contrast, the models estimated using FDI stock pass the

RESET test 15, that is, the RESET test provides no evidence of misspecification of the gravity

equations estimated using the FDI stocks. Therefore, these results support our hypotheses

that FDI stock data are more appropriate than flows in econometric FDI analysis as these

variables include more information.

We have subjected our estimates to robustness checks. First, since the regional distri-

bution of FDI reveals that Madrid concentrates the bulk of the foreign investment received

in Spain, namely 63%, we also examine the FDI determinants excluding Madrid from our

sample in order to check the robustness of our results. Results are reported in table A.2 in

the appendix. We found that the significance, sign and magnitude of the coefficients are not

altered substantially. Notice that the difference in factor endowments now becomes signif-

icant. The RESET test provides no evidence of misspecification of the equations estimated

using FDI stock data while FDI inflows specifications appeared to be wrongly-specified.

Second, our specifications using FDI inflows were conducted for the period 1995-2013.

However, as FDI stock data are only available from 2004 to 2013, we think that it could be

interesting to reduce the sample and repeat the baseline estimations for FDI inflows for this

shorter period (2004-2013). Table A.3 in the appendix shows that the sign and magnitude

of the coefficients remain considerably stable. Moreover, the specifications do not pass the

RESET test, that is, the RESET test provides evidence of misspecification of the models if

FDI inflows are used instead of stocks.15Model specifications in column (2) and (4) pass the RESET test at the 5% significance level

20

6. Concluding remarks

In the last decades, understanding the factors that determine FDI has attracted an intense

academic and policy-oriented interest. This paper feeds the discussion conducting an em-

pirical investigation to identify the main macroeconomic driving forces for FDI activity

directed towards the Spanish regions. In addition to the regional dimension itself, this pa-

per makes two contributions to the literature: first, our research represents one of the first

attempts to find the main determinants of inward FDI into Spanish regions using FDI stock

instead of flows. We have compared, for the sake of robustness, the estimation results us-

ing the two definitions of FDI and concluded that the stocks model was the correct model

specification. Second, we use the PPML estimator which produces unbiased and consistent

estimates when the dependent variable includes a large proportion of zero observations, as

it turns out to be our case.

The empirical analysis revealed the following allocation patterns: FDI locational strate-

gies in the Spanish regions are determined significantly by market size, meaning that FDI

goes to regions with larger consumer demand and better market opportunities. In addi-

tion, labour productivity at the host regions was found to be also an important factor of

FDI location in all of the estimated models. Moreover, trade openness appeared to be a ro-

bust and significant determinant of FDI location at the regional level. This finding supports

the view of a complementary relationship between trade and FDI, a result common to the

majority of empirical studies on trade-FDI linkages. Furthermore, the analysis showed that

the allocation of FDI depends upon the geographical position of each region, in the sense

that FDI tends to be placed in coastal areas. Surprisingly, human capital is found to be

significant but, in contrast to what we expected, has a negative sign. This means that FDI

in Spanish regions is oriented to activities with a very low value added and hence, seeks

for cheap and less qualified workers. Finally, the global financial crisis exerts a negative

significant impact on FDI in the models in which FDI stock is used as the dependent vari-

able indicating the reduction in inward FDI stock since the outbreak of the crisis. We get,

therefore, empirical support for all of our hypotheses with the exception of H3 (human

capital) in which we obtain the opposite sign.

Overall, our empirical findings show that the attractiveness of the Spanish regions to

foreign investors is determined by gravity factors related to market conditions (such as

market size and labour productivity), geographic position and by trade liberalization poli-

cies. Hence, up to now economic integration has had a positive impact on the FDI received

21

in Spain. A future challenge would be to improve labour productivity and encourage

projects that may incorporate more R&D and, therefore, increase their value added.

22

References

Agiomirgianakis, G., Asteriou, D., and Papathoma, K. (2003). The determinants of Foreign Direct Investment:

a Panel Data Study for the OECD Countries.

Anderson, J. E. and van Wincoop, E. (2003). Gravity with Gravitas : A Solution to the Border Puzzle. The

American Economic Review, 93(1):170–192.

Ang, J. B. (2008). Determinants of foreign direct investment in Malaysia. Journal of Policy Modeling, 30(1):185–

189.

Assunçao, S., Forte, R., and Teixeira, A. (2011). Location Determinants of FDI: a Literature Review.

Bajo-Rubio, O. and Sosvilla-rivero, S. (1994). An Econometric Analysis of Foreign Direct Investment in Spain,

1964-89. Southern Economic Journal, 61(1):104–120.

Baldwin, R. E., Di Nino, V., Fontagné, L., De Santis, R. A., and Taglioni, D. (2008). Study on the Impact of the

Euro on Trade and Foreign Direct Investment. Number Economic Papers 321.

Barba Navaretti, G. and Venables, A. J. (2004). Multinational Firms in the World Economy. Princeton University

Press, Princeton, New Jersey.

Bénassy-Quéré, A., Coupet, M., and Mayer, T. (2007). Institutional determinants of foreign direct investment.

World Economy, 30(5):764–782.

Bergstrand, J. H. and Egger, P. (2004). A Knowledge-and-Physical-Capital Model of International Trade, Foreign

Direct Investment, and Outsourcing: Part I, Developed Countries.

Bergstrand, J. H. and Egger, P. (2007a). A knowledge-and-physical-capital model of international trade flows,

foreign direct investment, and multinational enterprises. Journal of International Economics, 73(2):278–308.

Bergstrand, J. H. and Egger, P. (2007b). A knowledge-and-physical-capital model of international trade flows,

foreign direct investment, and multinational enterprises. Journal of International Economics, 73(2):278–308.

Bevan, A. A. and Estrin, S. (2004). The determinants of foreign direct investment into European transition

economies. Journal of Comparative Economics, 32(4):775–787.

Blonigen, B. A. (2005). A review of the empirical literature on FDI determinants. Atlantic Economic Journal,

33(4):383–403.

Blonigen, B. A., Davies, R. B., and Head, K. (2003). Estimating the Knowledge-Capital Model of the Multina-

tional Enterprise. The American Economic Review, 91(3):693–708.

Blonigen, B. A., Davies, R. B., Waddell, G. R., and Naughton, H. T. (2007). FDI in space: Spatial autoregressive

relationships in foreign direct investment. European Economic Review, 51(5):1303–1325.

Blonigen, B. a. and Piger, J. M. (2014). Determinants of foreign direct investment. Canadian Journal of Economics,

47(3):775–812.

23

Boateng, A., Hua, X., Nisar, S., and Wu, J. (2015). Examining the determinants of inward FDI: Evidence from

Norway. Economic Modelling, 47:118–127.

Brainard, S. L. (1997). An Empirical Assessment of the Proximity-Concentration Trade-off Between Multina-

tional Sales and Trade. American Economic Review, 87(4):520–544.

Bruno, R., Campos, N., Estrin, S., and Tian, M. (2016). Technical Appendix to â The Impact of Brexit on

Foreign Investment in the UK ’ Gravitating Towards Europe : An Econometric Analysis of the FDI Effects

of EU Membership.

Busse, M. and Hefeker, C. (2007). Political risk, institutions and foreign direct investment. European Journal of

Political Economy, 23(2):397–415.

Canton, E. and Solera, I. (2016). Greenfield Foreign Direct Investment and Structural Reforms in Europe:What

Factors Determine Investments? European Economy Discussion Paper, 8022.

Carr, B. D. L., Markusen, J. R., and Maskus, K. E. (2016). American Economic Association Estimating the

Knowledge-Capital Model of the Multinational Enterprise : Reply Author ( s ): David L . Carr , James R .

Markusen and Keith E . Maskus Source : The American Economic Review , Vol . 93 , No . 3 ( Jun ., 2003 ),.

93(3):995–1001.

Chan, M. W. L., Hou, K., Li, X., and Mountain, D. C. (2014). Foreign direct investment and its determinants: A

regional panel causality analysis. Quarterly Review of Economics and Finance, 54(4):579–589.

Chidlow, A., Salciuviene, L., and Young, S. (2009). Regional determinants of inward FDI distribution in Poland.

International Business Review, 18(2):119–133.

Demekas, D. G., Horváth, B., Ribakova, E., and Wu, Y. (2007). Foreign direct investment in European transition

economies-The role of policies. Journal of Comparative Economics, 35(2):369–386.

Dimitropoulou, D., McCann, P., and Burke, S. P. (2013). The determinants of the location of foreign direct

investment in UK regions. Applied Economics, 45(27):3853–3862.

Dunning, J. (1977). Trade, Location of Economic Activity and the MNE: a Search for an Eclectic Approach.

Reading: University of Reading, Department of Economics.

Dunning, J. H. (1979). Explaining Changing Patterns of International Production: In Defence of the Eclectic

Theory. Oxford Bulletin of Economics and Statistics, 41(4)(Barlow 1953):269–295.

Dunning, J. H. (2000). The eclectic paradigm as an envelope for economic and business theories of MNE

activity. International Business Review, 9(2):163–190.

Economou, F., Hassapis, C., Philippas, N., and Tsionas, M. (2016). Foreign Direct Investment Determinants in

OECD and Developing Countries. Review of Development Economics, (June 2014):12–15.

EEAG (2014). The EEAG Report on the European Economy. 2:55–72.

24

Egea Román, M. P. and López Pueyo, C. (1991). Un estudio sobre la distribución geográfica de la inversión

extranjera directa en España. Información Comercial Española, 696-697:105–118.

Ekholm, K., Forslid, R., and Markusen, J. (2007). Export-platform Foreign Direct Investment. Journal of the

European Economic Association, 5(June):776–795.

Ethier, W. J. and Markusen, J. R. (1996). Multinational firms, technology diffusion and trade. Journal of Interna-

tional Economics, 41(1-2):1–28.

Faeth, I. (2009). Determinants of foreign direct investment - A tale of nine theoretical models. Journal of

Economic Surveys, 23(1):165–196.

Gouel, C., Guimbard, H., and Laborde, D. (2012). A Foreign Direct Investment database for global CGE models.

CEPII Working Paper 2012 - 09.

Gutiérrez Portilla, P. (2013). Foreign direct investment in Spain : Which are the determining factors?

Gutiérrez-Portilla, P., Maza, A., Villaverde, J., Hierro, M., and Es, P. G. (2016). Foreign direct investment in the

Spanish regions: What are the influencing factors? Investigaciones Regionales â Journal of Regional Research

Journal of Regional Research, 35(67):67–83.

Head, K. and Ries, J. (2008a). FDI as an outcome of the market for corporate control: Theory and evidence.

Journal of International Economics, 74(1):2–20.

Head, K. and Ries, J. (2008b). FDI as an outcome of the market for corporate control: Theory and evidence.

Journal of International Economics, 74(1):2–20.

Helpman, E. (1984). A Simple Theory of International Trade with Multinational Corporations. Journal of Political

Economy, 92(3):451–471.

Helpman, E. (1985). Multinational Corporations and Trade Structure. Review of Economic Studies, LII:443–457.

Helpman, E., Melitz, M. J., and Yeaple, S. R. (2003). Export versus FDI. National Bureau of Economic Research,

(January):18–28.

Hoang, H. H. and Goujon, M. (2014). Determinants of foreign direct investment in Vietnamese provinces: a

spatial econometric analysis.

Horstmann, I. J. and Markusen, J. R. (1987). Strategic Investments and the Development of Multinationals.

International Economic Review, pages 109–121.

Kandogan, Y. (2012). Regional foreign direct investment potential of the states within the US. Journal of

Economics and Business, 64(4):306–322.

Kersan-Skabic, I. and Tijanic, L. (2014). Regional Determinants of Foreign Direct Investments in Croatia.

Transylvanian Review of Administrative Sciences, (SI):70–89.

Kleinert, J. and Toubal, F. (2010). Gravity for FDI. Review of International Economics, 18(1):1–13.

25

Markusen, J. R. and Maskus, K. E. (2002). Discriminating Among Alternative Theories of the Multinational

Enterprise. Review of International Economics, 10(4):694–707.

Markusen, J. R. and Venables, A. J. (1998). Multinational firms and the new trade theory. Journal of International

Economics, 46(2):183–203.

Martínez-Martín, J. (2011). General equilibrium long-run determinants for Spanish FDI: A spatial panel data

approach. SERIEs, 2(3):305–333.

Mitze, T., Alecke, B., and Untiedt, G. (2012). Trade, FDI and Cross-Variable Linkages: A German (Macro-)

Regional Perspective. MPRA Munich Ersonal REPEc Archive, (41193).

Paniagua, J. (2011). FDI gravity equation: Models, estimations and zeros. Catholic Universyty of Valencia.

Pantulu, J. and Poon, J. P. H. (2003). Foreign direct investment and international trade: evidence from the US

and Japan. Journal of Economic Geography, 3(3):241–259.

Pelegrín, A. (2002). Inversión extranjera directa. Factores determinantes de la localización regional. Papeles de

Economía Española, 93:122–133.

Pelegrín, A. (2003). Regional Distribution of Foreign Manufacturing Investment in Spain Do agglomeration

economies matter ? Documents de treball IEB, (6)(1).

Pelegrín, A. and Bolancé, C. (2008). Regional Foreign Direct Investment in Manufacturing. Do Agglomeration

Economies Matter? Regional Studies, 42(4):505–522.

Ramondo, N. (2014). A quantitative approach to multinational production. Journal of International Economics,

93(1):108–122.

Ramondo, N. and Rodríguez-Clare, A. (2013). Trade, Multinational Production, and the Gains from Openness.

Journal of Political Economy, 121(2):273–322.

Ramondo, N., Rodríguez-Clare, A., and Tintelnot, F. (2015). Multinational Production: Data and Stylized Facts

Natalia. American Economic Review. Papers and Proceedings, 105(5):530–536.

Ramsey, J. (1969). Testing for Specification Errors in Classical Linear Least-Squares Regression Analysis. Journal

of the Royal Statistical Society. Series B (Methodological), 31(2):350–371.

Ravinthirakumaran, K., Selvanathan, E. A., Selvanathan, S., and Singh, T. (2015). Determinants of Foreign

Direct Investment in Sri Lanka. South Asia Economic Journal, 16(2)(March):233–256.

Rodríguez, X. A. and Pallas, J. (2008). Determinants of foreign direct investment in Spain. Applied Economics,

40(19):2443–2450.

Silva, J. S. and Tenreyro, S. (2006). The log of gravity. The Review of Economics and Statistics, 88(4):641–658.

Talamo, G. M. (2007). Institution , FDI and the Gravity Model.

26

Villaverde, J. and Maza, A. (2012). Foreign direct investment in Spain: Regional distribution and determinants.

International Business Review, 21(4):722–733.

Wacker, K. M. (2013). On the measurement of FDI and its relationship to activities of multinational corpora-

tions. ECB Working Paper No.1614.

27

Table 1: Countries included in the study

Countries inside the EU Countries outside the EU

Germany Mexico

France United States

Netherlands

Portugal

United Kingdoma

Sweden

Notes: Included as countries of origin.

Table 2: Data description and source

Variable name Description Source

FDIinijt Real inward FDI stock, in millions of euros Datainvex

FDIinflowijt Real inward FDI flows, in millions of euros Datainvex

MSitReal Gross Domestic Product per capita, in millions of

eurosINE

LPit GDP per total employment INE

HCit Share of population with Tertiary Educationb

Valencian Institute of

Economic Research

(Ivie)c

OPitSum of bilateral exports and imports as a share of GDP

(%),in millions of eurosDataComex

RFE Differences in per capita GDP. INE, World Bank

LANDLOCKED (0,1)Dummy for landlocked regions.

aSince the 2016 referendum vote to leave the EU, the UK is on course to leave the EU by April 2019.bTertiary Education includes "FP II", "Anteriores al Superior" and "Superiores".cFundación Bancaja e Ivie (Instituto Valenciano de Investigaciones Económicas). Capital Hu-

mano en España y su distribución provincial. Enero de 2014. Database available on:http://www.ivie.es/es/banco/caphum/series.php.

28

Table 3: Descriptive statistics

Variable Mean Std.dev. Min. Max. Obs.

FDIin Overall 1478.61 4800.943 0 41063.63 N=1360

Between 4682.864 0 31924.67 n =136

Within 1124.75 -8539.056 13960 T=10

FDIinflow Overall 79.38222 522.7376 0 14032.73 N=1360

Between 292.6419 0 2767.109 n =136

Within 433.8 -2339.91 11345.01 T=10

MS Overall 10.07768 0.2039063 9.62118 10.47492 N=1360

Between 0.1931668 9.717784 10.40409 n=136

Within 0.0671674 9.919306 10.17744 T=10

LP Overall 11.00891 0.8644262 9.235526 13.54135 N=1360

Between 0.863671 9.386642 13.50913 n=136

Within 0.0790247 10.09886 11.1402 T=10

HC Overall -1.658593 0.2183659 -2.176158 -1.203947 N=1360

Between 0.2063208 -1.97792 -1.251782 n=136

Within 0.0734665 -1.89477 -1.503462 T=10

OP Overall -4.363702 1.228567 -8.308914 -1.688068 N=1360

Between 1.206123 -7.684219 -1.850836 n=136

Within 0.2535299 -5.717792 -3.089556 T=10

RFE Overall 9.078011 0.875026 4.465661 10.26282 N=1360

Between 0.7811735 5.71967 10.15922 n=136

Within 0.3993493 6.123605 10.8737 T=10

LANDLOCKED Overall 0.4117647 0.492334 0 1 N=1360

Between 0.4939724 0 1 n=136

Within 0 0.4117647 0.4117647 T=10

Notes: All the continuos variables are in logarithms apart from inward FDI stockand flow.

29

Table 4: Pearson’s Correlations

Variable (1) (2) (3) (4) (5) (6)

(1) FDIin 1.0000

1058

(2) MS 0.4202* 1.0000

0.0000

1058 2584

(3) LP 0.5463* 0.4963* 1.0000

0.0000 0.0000

1058 2584 2584

(4) HC 0.3403* 0.7569* 0.4235* 1.0000

0.0000 0.0000 0.0000

1058 2584 2584 2584

(5) OP 0.3375* 0.2204* 0.1216* 0.2497* 1.0000

0.0000 0.0000 0.0000 0.0000

1058 2584 2584 2584 2584

(6) RFE -0.3434* -0.2584* -0.2076* -0.1505* -0.3848* 1.0000

0.0000 0.0000 0.0000 0.0000 0.0000

1058 2584 2584 2584 2584 2584

Notes: All the variables are in logarithms.

30

Table 5: Estimates of the Spanish inward FDI determinants. (I)

Dependent variable: Inward FDI stock

(1) (2) (3) (4)

Variables

MS 3.673∗ ∗ ∗ 3.379∗ ∗ ∗ 2.905∗ 3.379∗ ∗ ∗(1.402) (1.256) (1.505) (2.691)

LP 1.343∗ ∗ ∗ 1.660∗ ∗ ∗ 1.667∗ ∗ ∗ 1.660∗ ∗ ∗(0.110) (0.131) (0.134) (12.679)

HC −3.458∗ ∗ ∗ −2.986∗ ∗ ∗ −2.923∗ ∗ ∗ −2.986∗ ∗ ∗(1.163) (0.965) (0.992) (−3.096)

OP 0.489∗∗ 0.423∗∗ 0.386∗∗ 0.423∗∗(0.191) (0.188) (0.186) (2.249)

LANDLOCKED −0.993∗ ∗ ∗ −0.979∗ ∗ ∗ −0.993∗ ∗ ∗(0.260) (0.257) (−3.816)

RFE −0.153

(0.130)

Crisis −0.655∗∗(−2.124)

R2(Adjusted) 0.859 0.869 0.862 0.869

Investing country FE (jt) Yes Yes Yes Yes

Year FE Yes Yes Yes Yes

Observations 1360 1360 1360 1360

RESET test p− values 0.9735 0.0619 0.1218 0.0619

Notes: Country pair clustered standard errors are in parentheses. ∗, ∗∗ and ∗∗∗ denote significanceat 10%, 5%, and 1% significance levels, respectively.

31

Table 6: Estimates of the Spanish inward FDI determinants. (II)

Dependent variable: FDI inflow

(1) (2) (3) (4) (5)

Variables

MS 2.726*** 2.492*** 2.121** 2.492*** 2.492***

(0.884) (0.832) (0.894) (2.994) (2.994)

LP 1.621*** 1.891*** 1.893*** 1.891*** 1.891***

(0.113) (0.157) (0.155) (12.072) (12.072)

HC -3.929*** -3.443*** -3.414*** -3.443*** -3.443***

(0.745) (0.651) (0.653) (-5.288) (-5.288)

OP 0.729*** 0.670*** 0.636*** 0.670*** 0.670***

(0.193) (0.182) (0.179) (3.680) (3.680)

LANDLOCKED -0.871*** -0.853*** -0.871*** -0.871***

(0.259) (0.262) (-3.360) (-3.360)

RFE -0.132

(0.090)

Crisis 1.979** 0.993***

(2.386) (3.666)

EU Enlargement -0.994*

(-1.693)

EMU 1.980***

(3.117)

R2(Adjusted) 0.885 0.877 0.877 0.877 0.877

Investing country FE (jt) Yes Yes Yes Yes Yes

Year FE Yes Yes Yes Yes Yes

Observations 2584 2584 2584 2584 2584

RESET test p− values 0.0000 0.0000 0.0000 0.0000 0.0000

Notes: Country pair clustered standard errors are in parentheses. ∗, ∗∗ and∗∗∗ denote significance at 10%, 5%, and 1% significance levels, respectively.The period of analysis is 1995-2013.

32

Figure 1: Foreign Direct Investment in Spain.

0

100000

200000

300000

400000

500000

600000

700000

0

10000

20000

30000

40000

50000

60000

70000

80000

90000

1970 1972 1974 1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014

US

Do

llars

, in

mill

ion

s

US

Do

llars

, in

mill

ion

s

Inward Stock Inflow

Source:UNCTAD database.

33

Figure 2: Spatial Distribution of Average Inward FDI Stock (2004-2014).

(469.14,15738.73](315.50,469.14](151.70,315.50][14.13,151.70]

Figure 3: Main investors in Spain: Average FDI for the 2004-2014 period, in percent.

19%

16%

16%11%

7%

6%

5%

4%

4%

2%2%

2% 1%

1%1%

4%US

France

UK

Germany

Mexico

Netherlands

Luxembourg

Switzerland

Portugal

Sweden

Italy

Japan

China

Canada

Belgium

Others

34

APPENDIX A

Table A.1: Descriptive statistics for the 1995-2013 period.

Variable Mean Std.dev. Min. Max. Obs.

FDIinflow Overall 80.49317 570.5805 0 20050.97 N=2584

Between 299.2155 0 2975.533 n =136

Within 486.4731 -2744.054 17155.93 T=19

MS Overall 9.999757 0.2371053 9.306824 10.47492 N=2584

Between 0.2033882 9.594603 10.31539 n=136

Within 0.1230466 9.711978 10.20416 T=19

LP Overall 10.97016 0.8775762 9.235526 13.54135 N=2584

Between 0.8728341 9.351216 13.44953 n=136

Within 0.1166603 10.40215 11.44349 T=19

HC Overall -1.822069 0.2981713 -2.691463 -1.203947 N=2584

Between 0.2164045 -2.160014 -1.399651 n=136

Within 0.2059164 -2.391546 -1.470742 T=19

OP Overall -4.356162 1.282803 -9.903712 -1.276218 N=2584

Between 1.24359 -8.14364 -1.73026 n=136

Within 0.331428 -7.513965 -2.011115 T=19

RFE Overall 9.074308 0.8717845 4.157723 10.52291 N=2584

Between 0.7453183 6.600978 10.07593 n=136

Within 0.4564864 5.618167 10.78682 T=19

LANDLOCKED Overall 0.4117647 0.4922482 0 1 N=2584

Between 0.4939724 0 1 n=136

Within 0 0.4117647 0.4117647 T=19

Notes: All the continuos variables are in logarithms apart from FDI inflow.

35

Table A.2: Robustness check: Estimates excluding the capital city region,Madrid.

Dependent variable: Inward FDI stock/inflow

(1) (2) (3) (4)

Variables

MS 3.548∗∗ 2.562∗ 3.041∗ ∗ ∗ 2.466∗ ∗ ∗(1.392) (1.334) (0.740) (0.786)

LP 1.871∗ ∗ ∗ 0.913∗∗ 2.208∗ ∗ ∗ 1.510∗ ∗ ∗(0.443) (0.395) (0.381) (0.373)

HC −3.578∗ ∗ ∗ −2.689∗ ∗ ∗ −4.053∗ ∗ ∗ −3.281∗ ∗ ∗(0.994) (0.916) (0.659) (0.645)

OP 0.684∗ ∗ ∗ 0.616∗ ∗ ∗ 0.670∗ ∗ ∗ 0.642∗ ∗ ∗(0.198) (0.179) (0.180) (0.163)

LANDLOCKED −1.672∗ ∗ ∗ −1.201∗ ∗ ∗(0.329) (0.257)

RFE −0.432∗ ∗ ∗ −0.234∗∗(0.121) (0.116)

Crisis −0.452 2.544∗ ∗ ∗(0.313) (0.795)

R2(Adjusted) 0.495 0.520 0.400 0.397

Investing country FE (jt) Yes Yes Yes Yes

Year FE Yes Yes Yes Yes

Observations 1200 1200 2280 2280

RESET test p− values 0.0061 0.6555 0.0000 0.0000

Notes: Country pair clustered standard errors are in parentheses. ∗, ∗∗ and ∗∗∗ denote significanceat 10%, 5%, and 1% significance levels, respectively. Columns (1) and (2) use inward FDI stock asthe dependent variable while columns (3) and (4) use FDI inflows.

36

Table A.3: Estimates of the FDI inflows deter-minants for the period 2004-2013.

Dependent variable: FDI inflow

(1) (2)

Variables

MS 4.182*** 3.326***

(1.020) (1.149)

LP 1.678*** 1.972***

(0.125) (0.178)

HC -5.658*** -4.961***

(0.922) (0.843)

OP 0.594*** 0.500***

(0.192) (0.184)

LANDLOCKED -0.902***

(0.336)

RFE -0.112

(0.104)

Crisis 0.290

(0.392)

R2(Adjusted) 0.893 0.886

Investing country FE (jt) Yes Yes

Year FE Yes Yes

Observations 1360 1360

RESET test p− values 0.0000 0.0000

Notes: Country pair clustered standard errors are inparentheses. ∗, ∗∗ and ∗∗∗ denote significance at 10%,5%, and 1% significance levels, respectively.

37

Tabl

eA

.4:E

mpi

rica

llit

erat

ure

revi

ewon

FDI

dete

rmin

ants

Art

icle

Leve

lFD

Ide

stin

atio

nD

epen

dent

vari

able

Tim

epe

riod

Esti

mat

ion

tech

niqu

eFD

Ide

term

inan

ts

(+)

(-)

Agi

omir

gian

akis

etal

.(20

03)

Cou

ntry

20O

ECD

Shar

eof

FDI

toG

DP

1975

-199

7Po

oled

OLS

,FE,

RE

Hum

anca

pita

lTr

ade

regi

me

Infr

astr

uctu

reA

gglo

mer

atio

ns

Béna

ssy-

Qué

réet

al.(

2007

)C

ount

ryO

ECD

and

emer

ging

and

de-

velo

ping

econ

omie

s

Log

ofBi

late

ral

FDI

Stoc

k20

00/1

985-

2000

Gra

vity

appr

oach

:cr

oss-

sect

ion

and

pane

lre

gres

-si

ons,

Pois

son

QM

LE

Inst

itut

ions

(Bur

eauc

racy

,C

orru

ptio

n,In

-fo

rmat

ion,

Bank

-in

gse

ctor

,Le

gal

inst

itut

ions

)

Wea

kca

pita

lcon

-ce

ntra

tion

Empl

oym

ent

prot

ecti

on

Tala

mo

(200

7)C

ount

ry61

coun

-tr

ies

(OEC

Dan

dno

n-O

ECD

)

Log

ofBi

late

ral

FDI

Flow

s19

80-2

001

Gra

vity

appr

oach

:Po

oled

OLS

(wit

hye

ardu

mm

ies

and

coun

try

fixed

effe

cts)

Qua

lity

ofco

rpo-

rate

gove

rnan

cein

stit

utio

nsan

dm

echa

nism

sSh

areh

olde

rpr

o-te

ctio

nO

penn

ess

Brun

oet

al.

(201

6)C

ount

ry34

OEC

DBi

late

ral

Inw

ard

FDI

Flow

san

dSt

ocks

1985

-201

3G

ravi

tyap

proa

ch:O

LS,P

ois-

son,

Hec

kman

EUm

embe

rshi

pIn

dust

rysh

ares

Impo

rtsh

ares

Per

capi

taG

DP(

host

)Ex

port

shar

es

Econ

omou

etal

.(2

016)

Cou

ntry

24O

ECD

and

22de

velo

ping

(non

-O

ECD

)

FDI

Inw

ard

Flow

s19

80-2

012

FE,

Are

l-la

noâB

over

/Blu

ndel

lâBo

nddy

nam

icpa

nelm

odel

Lagg

edFD

IM

arke

tsi

zeG

ross

capi

tal

for-

mat

ion

Cor

pora

teta

xa-

tion

Labo

rco

stIn

stit

utio

nalv

ar.

38

Art

icle

Leve

lFD

Ide

stin

atio

nD

epen

dent

vari

able

Tim

epe

riod

Esti

mat

ion

tech

niqu

eFD

Ide

term

inan

ts

(+)

(-)

Beva

nan

dEs

trin

(200

4)C

ount

ryC

EEC

Bila

tera

lFD

IFl

ows

1994

-200

0G

ravi

tyap

proa

ch:R

EU

nit

labo

rco

sts

Gra

vity

fact

ors

Mar

ket

size

Prox

imit

yEU

anno

unce

-m

ents

Dem

ekas

etal

.(2

007)

Cou

ntry

16C

EEC

Bila

tera

lFD

IFl

ows

and

Stoc

ks/A

ggre

gate

nonp

riva

tiza

tion

-re

late

dFD

ISt

ock

2000

-20

02/1

995-

2003

Bila

tera

lcr

oss-

sect

ion,

Sys-

tem

GM

MG

ravi

tyfa

ctor

sLi

bera

lTr

ade

Reg

ime

Infr

aest

ruct

ure

Uni

tla

bor

cost

sC

orpo

rate

tax

burd

enIm

port

tari

ffs

Can

ton

and

Sol-

era

(201

6)C

ount

ry28

EUBi

late

ral

Gre

en-

field

FDI

Flow

s20

03-2

014

Gra

vity

appr

oach

:Hec

kman

Busi

ness

clim

ate

Prod

uct

mar

ket

regu

lati

ons

Ang

(200

8)C

ount

ryM

alay

sia

FDI

Inflo

ws

1960

â200

5C

oint

egra

tion

(EC

M)

Rea

lGD

PG

DP

grow

thFi

nanc

ial

deve

l-op

men

tIn

frae

stru

ctur

eTr

ade

Ope

nnes

sM

acro

econ

omic

unce

rtai

nty

Cor

pora

teta

xra

teER

appr

ecia

tion

Boat

eng

etal

.(2

015)

Cou

ntry

Nor

way

FDI

Inflo

ws

1986

â200

8C

oint

egra

tion

(FM

OLS

,VA

R/V

ECM

)R

ealG

DP

Sect

orG

DP

ER Trad

eO

penn

ess

Mon

eysu

pply

Infla

tion

Une

mpl

oym

ent

Inte

rest

rate

39

Art

icle

Leve

lFD

Ide

stin

atio

nD

epen

dent

vari

able

Tim

epe

riod

Esti

mat

ion

tech

niqu

eFD

Ide

term

inan

ts

(+)

(-)

Rav

inth

irak

umar

anet

al.(

2015

)C

ount

rySr

iLan

kaFD

Iin

flow

s19

78â2

013

Uni

tro

ots,

Coi

nteg

rati

on(E

CM

,AR

DL)

Mar

ket

size

Trad

eO

penn

ess

Infr

aest

ruct

ure

Wag

ePo

litic

alin

stab

il-it

y

Chi

dlow

etal

.(2

009)

Reg

iona

lPo

lish

regi

ons

Prob

abili

tyof

ei-

ther

inve

stin

gor

not

inan

ygi

ven

regi

on

2005

Mul

tino

mia

llog

itm

odel

Kno

wle

dge-

seek

ing

fact

ors

Mar

ket

Agg

lom

erat

ion

fact

ors

Effic

ienc

yG

eogr

aphi

calf

ac-

tors

Kan

doga

n(2

012)

Reg

iona

lU

Sst

ates

FDI

stoc

kFD

Iem

ploy

men

t20

06M

ulti

ple

regr

essi

onan

alys

isU

nem

ploy

men

tra

teM

arke

tsi

zePe

rca

pita

in-

com

eIn

com

egr

owth

Stat

ere

gula

tion

s

Dim

itro

poul

ouet

al.(

2013

)R

egio

nal

UK

regi

ons

Prob

abili

tyof

ei-

ther

inve

stin

gor

not

inan

ygi

ven

regi

on

1997

-200

3M

ulti

nom

ial

and

cond

itio

nal

logi

tm

odel

sR

egio

nal

spec

ial-

izat

ion

Cha

net

al.(

2014

)R

egio

nal

29C

hine

sere

gion

sFD

Iin

flow

s19

95â2

010

Gra

nger

caus

alit

yG

DP

grow

thG

row

thin

loca

lin

frae

stru

ctur

ean

din

vest

men

t

40

Art

icle

Leve

lFD

Ide

stin

atio

nD

epen

dent

vari

able

Tim

epe

riod

Esti

mat

ion

tech

niqu

eFD

Ide

term

inan

ts

(+)

(-)

Ker

san-

Skab

ican

dTi

jani

c(2

014)

Reg

iona

lC

roat

iaFD

Ist

ock

2000

-201

0R

EFG

LSSy

stem

GM

M

Educ

atio

nIn

frae

stru

ctur

eM

anuf

actu

ring

indu

stry

Are

asof

spec

ial

stat

eco

ncer

nC

apit

alci

ty

Une

mpl

oym

ent

EUbo

rder

Bajo

-Rub

ioan

dSo

svill

a-ri

vero

(199

4)

Cou

ntry

Spai

nG

ross

Agr

egat

eFD

IIn

flow

s19

64-1

989

Coi

nteg

rati

onR

ealG

DP

Infla

tion

rate

Trad

eba

rrie

rsLa

gged

fore

ign

capi

tals

tock

Pele

grín

(200

3)R

egio

nal

Spai

nG

ross

Effe

ctiv

eFo

reig

nIn

vest

-m

ent

1993

-200

0R

EG

LSO

LS

Man

ufac

turi

ngag

glom

erat

ion

R&

Dac

tivi

ties

Skill

edla

bor

Con

gest

ion

cost

s

Pele

grín

and

Bola

ncé

(200

8)R

egio

nal

Spai

nG

ross

Effe

ctiv

eFo

reig

nIn

vest

-m

ent

Per

Cap

ita

1995

â200

0R

EFE

Agg

lom

erat

ion

econ

omie

sM

anuf

actu

ring

acti

vity

R&

Dac

tivi

ties

Endo

wm

ent

Rod

rígu

ezan

dPa

llas

(200

8)R

egio

nal

Spai

nG

ross

Effe

ctiv

eIn

vest

men

t19

93-2

002

GLS

W2S

LSR

ealG

DP

Hum

anca

pita

lEx

port

pote

ntia

lD

iffer

enti

albe

-tw

een

labo

rpr

oduc

tivi

tyan

dth

eco

stof

labo

r

41

Art

icle

Leve

lFD

Ide

stin

atio

nD

epen

dent

vari

able

Tim

epe

riod

Esti

mat

ion

tech

niqu

eFD

Ide

term

inan

ts

(+)

(-)

Mar

tíne

z-M

artín

(201

1)R

egio

nal

50co

un-

trie

sA

ggre

gate

dG

ross

Effe

ctiv

eO

utflo

w

1993

â200

4G

ravi

tyap

proa

ch:

Pool

edO

LS,

RE,

FE,

2WFE

,Sp

atia

lPa

nelM

odel

s(S

AR

,SEM

)

Spat

ial

inte

rde-

pend

ence

Vill

aver

dean

dM

aza

(201

2)R

egio

nal

Spai

nG

ross

Effe

ctiv

eFo

reig

nIn

vest

-m

ent

Per

Cap

ita

1995

-20

05/2

008

GLS

Two-

stag

eG

LSEc

onom

icpo

ten-

tial

Labo

rco

ndit

ions

Com

peti

tive

ness

Gut

iérr

ez-

Port

illa

etal

.(2

016)

Reg

iona

lSp

ain

Flow

sof

Inw

ard

Gro

ssFD

Ias

Per-

cent

age

ofG

DP

1997

-201

3G

MM

GLS

Mar

ket

size

Hum

anca

pita

l∗W

ages

Cap

ital

city

42