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7/30/2019 Presentation Feb22

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FDI in space: Spatial autoregressive relationships in

foreign direct investment

Students:

Iurii BerezhnoiEgor Cusmaunsa

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Motivation

Since 1980, world wide foreign direct investment (FDI) has grown at aremarkable rate. According to Markusen (2002), in the latter half of the1990s FDI flows grew annually by nearly 32% . Thus it drivesdevelopment of formal economic models of multinationalenterprises (MNEs ) and increased empirical investigation of factorsdriving FDI patterns.

The Main empirical determinants of FDI are market size and distance

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General-Equilibrium

Model

vertical FDI,MNEs - desire

to accesscheaperfactor inputs

abroad

Helpman (1984)

horizontalFDI,

MNEs substitutefor export flows

Markusen (1984) andHelpman (1984).

two-country frameworkmodels


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Weakness Solutions

reliance on the two-country (or

bilateral) framework

relax the two-country

assumption

Solutions: Export-platform FDI complex vertical

Idea: Parent country invest in aparticular host countryincluding third markets

exports of inputs tothird markets isprocessing beforebeing shipped to itsfinal destination

Authors: Ekholm et al. (2003), Yeaple(2003), Bergstrand andEgger (2004)

Baltagi et al

FDI decision are multilateral innature (i.e. not independent

across host countries)

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Research Background

What extent does omission of spatialinteractions bias the coefficients on thetraditional regressor matrix in empirical FDIstudies?

How robust are estimated spatial relationships inFDI patterns across specifications and samples?

To what extent can we uncover evidence of varioustheories of FDI using these techniques and availabledata?

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To explore these issues, we use various samples ofUS outbound FDI from 1983 through 1998.

We find that the estimated relationships oftraditional determinants of FDI aresurprisingly robust to the inclusion of terms tocapture spatial interdependence, even though

empirical patterns in the data suggest that suchinterdependence can itself be significant;

analysis also reveals that both the traditionaldeterminants of FDI and the estimated spatialinterdependence are sensitive to the sample of

countries examined;

Data & Findings

DATA

Findings

Input get Results

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Spatial autoregression (SAR)

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Main goal: How MNE motivations may generate importantspatial relationships in the data that may not be adequatelycontrolled for using standard econometric techniques onbilateral-country pairs.

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Methodology & Data

Where: FDI is an nx1 vector with j equal to FDI from the US(parent

country) to host country j; Log-linear form, leads to well behaved residuals Blonigen and

Davies (2004)

Host Variables - standard gravity-model variables for the hostcountries (GDP, population, distance between the parent andhost countries, and trade/investment friction variables), as wellas a measure of skilled-labor endowments.

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Integrated Model

Where: Surrounding-Market Potential variable broadly, where for a country j

it is defined as the sum of inverse-distance-weighted GDPs of allother kj countries in the world for which we can obtain GDP data, byyear.

*W*FDI is the spatial autoregression term, where W is the spatial lagweighting matrix and is a parameter to be estimated, which will

indicate the strength and sign of the spatial relationship in FDI.

W is a block-diagonal matrix of dimension n x n, with each blockcapturing a single years observations.

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Integrated Model

(,) defines the functional form of the weights, declining in the

distance, ,, between any two host countries i and j

Time invariant distance implies that:

The shortest distance is equal to 173 km, so that (,) is adjusted

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Methodology & Data

Table 2 provides a list of the 35 included countries(20 of which are OECD), as wellas summary statistics of the variables in our data from 1983 through 1998.

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Base results

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very strong rejection in the data that host GDP and surrounding-marketpotential have identical effects on FDI activity;

GDP - positive coefficient, surrounding-market potential negativecoefficient;

spatial lag term is positive and significant;

5% increase in FDI into a host country lead to 10% increase in thedistance-weighted FDI going into surrounding markets.

inclusion of country dummies substantially eliminates the statistical and

economic significance of the spatial terms


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Conclusion

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spatial interdependence has been largely ignored by the empirical FDIliterature

traditional determinants of FDI are surprisingly robust to inclusion of terms

to capture spatial interdependence

estimates of cross-country determinants of FDI are not very robust tochanging the sample of countries

we find evidence suggestive of export-platform FDI for most industrieswithin the developed European countries

presentation feb22

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