Upload
-
View
217
Download
0
Embed Size (px)
Citation preview
7/30/2019 Presentation Feb22
1/12
FDI in space: Spatial autoregressive relationships in
foreign direct investment
Students:
Iurii BerezhnoiEgor Cusmaunsa
22/02/2013 1
7/30/2019 Presentation Feb22
2/12
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
222/02/2013 2
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
7/30/2019 Presentation Feb22
3/12
3
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)
22/02/2013
7/30/2019 Presentation Feb22
4/12
4
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?
22/02/2013
7/30/2019 Presentation Feb22
5/12
5
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
22/02/2013
7/30/2019 Presentation Feb22
6/12
Spatial autoregression (SAR)
6
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.
22/02/2013
7/30/2019 Presentation Feb22
7/127
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.
22/02/2013
7/30/2019 Presentation Feb22
8/128
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.
22/02/2013
7/30/2019 Presentation Feb22
9/129
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
22/02/2013
7/30/2019 Presentation Feb22
10/1210
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.
22/02/2013
l
7/30/2019 Presentation Feb22
11/1211
Base results
22/02/2013
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
7/30/2019 Presentation Feb22
12/1212
Conclusion
22/02/2013
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