GREENFIELD FOREIGN DIRECT INVESTMENT AN D MERGERS AND [630194]

GREENFIELD FOREIGN DIRECT INVESTMENT AN D MERGERS AND
ACQUISITIONS: FEEDBACK AND MACROECONOMIC EFFECTS*

César Calderón
Central Bank of Chile

Norman Loayza
The World Bank

Luis Servén
The World Bank

Abstract

FDI flows to developing countries surged in the 1990s , to become their lead ing source of external
financing. This rise in FDI volume was accompanied by a marked change in its composition: investment
taking the form of acquisition of existing assets (M&A) grew much more rapidly than investment in new
assets (“greenfield” FDI), particularly in countr ies undertaking extensive privatization of public
enterprises. This raises two issues. First, is the M&A b oom a one-time effect of privatization, or is it likely
to be followed by a rise in greenfield investment? Second, do these two types of FDI have different macroeconomic causes and consequences – in relation to aggregate investment and growth? This paper
focuses on establishing the stylized facts in terms of time precedence between both types of FDI,
investment and growth, using annual data for the pe riod 1987-2001 and a large sample of industrial and
developing countries. We find that in all samples hi gher M&A is typically followed by higher greenfield
investment, while the reverse is true only for developi ng countries. In industrial and developing countries
alike, both types of FDI lead domest ic investment, but not the reverse. Finally, neither type of FDI
appears to precede economic growth in either devel oping or industrial countries, but FDI does respond
positively to increases in the growth rate.

JEL classification codes : F43, F37, O16

World Bank Policy Research Working Paper 3192, January 2004

The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange
of ideas about development issues. An objective of the series is to ge t the findings out quickly, even if the
presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors.
They do not necessarily represent the view of the World Bank, its Executive Directors, or the countries they
represent. Policy Research Working Papers are available online at http://econ.worldbank.org.

* This research was supported by the World Bank’s Latin Ameri can Regional Studies program. We thank Linda Kaltani for able
research assistance.
Public Disclosure Authorized Public Disclosure Authorized Public Disclosure Authorized Public Disclosure Authorized

11. Introduction

The 1990s witnessed a dramatic surge in fore ign direct investment (henceforth FDI) to
developing countries. Net FDI flows to LDCs rose from 0.5 percent of their overall GDP in the
late 1980s to over 2.5 percent in 2000-2001. The FDI increase was particularly marked in Latin
America. In the context of a steep decline in ot her private external flow s, FDI became the leading
source of external financing to the developing world after 1994.
The causes of the boom have attracted consid erable attention, and several authors have
attempted to disentangle the role played by “ push” and “pull” factors in the process – i.e.,
declining real interest rates in industrial economies, and the improved investment environment in
developing countries following li beralization and reform of their economies, including the
decision to privatize state enterprises.1
Along with their rising volume, FDI inflows al so showed a major change in composition.
Specifically, foreign investment in LDCs relate d to the acquisition of existing assets – i.e.,
mergers and acquisitions, henceforth denoted M&A – saw its share in total FDI inflows rise from
virtually nothing in the la te 1980s to half of th e total in the late 1990 s. The rise was again
especially significant in Latin America, wher e in 2001-02 M&A accounted for over 50 percent of
total FDI inflows. The other co mponent of FDI, foreign investment primarily related to the
acquisition of new assets – commonly referred to as “greenfield” FDI – rose as well, but its share
in total FDI inflows to LDCs experienced a decline. In a num ber of developing economies,
especially Latin American ones, the rise in M&A foreign investment was largely driven by
privatization of state-owned enterprises, particularly in the utilities and financial services industries.

2However, the FDI boom has also raised two major concerns. The first one involves the
uncertain future prospects of FD I to developing countries, follo wing the near completion of the
privatization drive in major economies (most not ably in Latin America). As just noted, a
considerable portion of the FDI inflows rece ived by these economies over the last decade
reflected M&A transactions relate d to the acquisition of public en terprise assets, and hence the
end of privatization might be followed by a sh arp decline in FDI infl ows, which, given the
predominant role acquired by investment flows in overall external fina ncing during the late
1990s, could generate major external difficulties in these countries.
Whether this concern is warranted, however, de pends to a large extent on the relationship
between M&A and greenfield FDI. Specifically, if th e former tends to set the stage for the latter,
then stagnating M&A need not cause undue worries, because the surge in mergers in the 1990s is
likely to be followed by rising greenfield investme nt, thus ensuring the c ontinuation of external
financing in the coming years.
The second concern relates to the growth impact of FDI flows, which has attracted
renewed interest in the wake of the FDI boom. While the theoretical literature has pointed out that
FDI may boost growth, both by raising aggregate inve stment and through tec hnological spillovers
– i.e., technology transfers that go beyond those firms directly re ceiving foreign capital – the
empirical literature shows considerable disagreeme nt about the relevance of these impacts. On the
one hand, firm-level studies often find no significant productivity effects of FDI.2 On the other
hand, macroeconomic studies tend to conclude that FDI boosts growth vi a higher productivity

1 See for example Calvo and Reinhart (1996), Fernández-Arias and Montiel (1996), Fernández-Arias (2000), and
Albuquerque, Loayza, and Servén (2003).
2 Aitken and Harrison (1999), Kokko, Tansini and Zejan (1996), and Haddad and Harrison (1993) find no evidence
of productivity spillovers; Blömstrom and Sjoholm (1999) find no evidence of technology spillovers but do find
some evidence of productivity improvements stemming from greater competitive pressure in local markets.

3and/or physical investment,3 although some papers argue that this requires the destination
economy to satisfy certain conditions,4 and yet others find no significant impact of FDI on
investment or growth.5
There are two major difficulties with the inte rpretation of many of these results, however.
First, both micro and macro studies face problems of bi-directional causality: high-productivity
and high-growth firms and countries are more likely to attract FD I than the rest, so that the
empirical association between gr owth and FDI could well reflect reverse causation from the
former to the latter. To the extent that high inve stment itself also reflects high anticipated returns,
the same argument would apply to its close asso ciation with FDI often fo und in empirical studies
(e.g., Bosworth and Collins 1999). 6
The other difficulty concerns the lack of distinction between gr eenfield FDI and M&A.
Since the former involves mainly (although not only) new capital assets, while the latter is just a
transfer of existing ones, greenfield FDI would seem more likely to a ffect growth – if at all – via
increased physical investment, while M&A FDI would be more likely to do so via enhanced
productivity growth. In f act, the increased importance of M&A in total FDI flows in recent years
has been singled out as the likely cause of an observed weakening in the empirical FDI-
investment link in the 1990s (World Bank 2001). Thus, failure to distinguish between the two

3 See World Bank (2001) and the references listed therein.
4 For example, Borensztein, de Gregorio, and Lee (1998) find that the investment and growth impact of FDI is
significant only when the recipient economy possesses high levels of human capital. A similar argument in relation to the importance of financial development is made by Alfaro et al. (2002). In turn, Blomstrom, Lipsey and Zejan
(1996) conclude that FDI has a stronger positive impact on growth in high-income destination economies, while
Nair-Reichert and Weinhold (2001) find that this positive effect takes place only in open economies.
5 See for example Carkovic and Levine (2002).
6 Some micro and macro studies do control for simultaneity; see, e.g., Aitken and Harrison (1999) and Carkovic and
Levine (2002). Both studies find no significant growth effects of FDI, so that th e association between the two
variables would mainly reflect causation from growth to FDI. Indeed, as shown by Rangvid (2001) using a sample of
industrial and developing countries, growth and investment returns are very closely associated. Thus anticipations of higher growth should attract increased domestic and foreign investment. This line of argument is empirically pursued
by Calderón, Loayza and Servén (2001) to explain international capital flows.

4types of FDI flows in the face of large change s in their relative magnitude – such as those
witnessed over the last decade – could bias the inferences on the relations hip of total FDI with
investment and growth. The purpose of this paper is to address these concerns
7 by examining the link between the
two components of FDI flows – greenfield and M&A – and their respec tive relationship with
aggregate investment and growth in a large cross-country time- series data set. The main
objective of the analysis is to identify the fact s present in the data, ra ther than exploring the
ability of a particular model to explain the empirical regularities . Specifically, the paper focuses
on establishing the patterns of time precedence between FDI, investment and growth. Thus, it
follows an approach similar to those adopted by r ecent influential studies that have attempted to
determine the patterns of causation between saving, investment and gr owth (Carroll and Weil
1995; Blomstrom, Lipsey, and Zejan 1996; Attanasio, Picci and Scorcu 2000).
The paper extends the existing literature along two dimensions . First, it provides what to
the best of our knowledge is the first explora tion of the dynamic relation between greenfield and
M&A foreign investment.8 Second, it uncover s systematic differences between these two
components of FDI flows regardin g their respective rela tionship with investment and growth in
the destination economies.9 The paper performs extensive robustness checks by employing a
variety of econometric specificati ons and working with various country samples in order to allow

7 Although we will not pursue it here, we should also men tion a third concern recently raised by Fernández-Arias and
Hausmann (2000), according to which the b oom in FDI to developing countries wo uld reflect the sorry state of their
markets and institutions which forces domestic investors to sell off their local assets, rather than providing proof of
sound economic management, as had been argued in the past.
8 There are strands of the FDI literature that focus on ot her aspects of the separation between greenfield and M&A
FDI. Some of them investigate the determinants of the mode of entry by foreign firms –that is, greenfield vs.
M&As– (e.g. Blömstrom, Kokko, and Zejan 2000 and Görg 2000), while others analyze the welfare implications of various modes of entry on the host economy from a theoretical perspective (e.g. Mattoo, Olarreaga, and Saggi 2001
and Ferrett 2003).

5for possible heterogeneity across country groups – industrial economies, where FDI is
characterized by large inflows and outflows and a la rge share of M&A in total investment flows;
developing countries, where th e M&A share of total FDI is much lower, and outflows are
dwarfed by inflows; and Latin Am erica, where the FDI boom of th e 1990s has been most closely
associated with privatizat ion of public enterprises.
The paper is organized as follows. Section 2 introduces the main concepts and data
regarding the composition of FDI, and offers a br ief overview of recent tr ends in the volume and
structure of FDI across a large number of industrial and developi ng countries. Section 3 reports
the results of causality tests between the M&A and greenfield components of FDI, and between
each of them, domestic investment a nd GDP growth. Section 4 concludes.
2. FDI: Concepts, data and trends
Direct investment undertaken by foreign firms in a host country (i.e., the country of the
target firm whose assets are being ac quired) can take the form of either greenfield investment or
mergers and acquisitions (M&As), depending on whether the transaction involves mainly newly-
created assets coming under control of the foreign firms, or just a tr ansfer of existing assets from
local firms, respectively.
The fact that FDI represents just a financing flow, and not necessarily investment, is often
overlooked. The same applies to greenfield inve stment, which does not necessarily reflect the
acquisition of new fixed assets. Like FDI, green field investment includes all financial transfers
from a multinational’s headquarters to its subsidia ry (and back, in the case of outflows). These
could take the form of equity or loan financ ing. While most financia l transfers presumably

9 The received literature that investigates the relationshi p between FDI, domestic invest ment, and growth has focused
on total FDI. Studies of this type include Choe (2003), Basu, Chakraborty, and Reagle (2003), de Mello (1999),
Ericsson and Irandoust (2001), Agosin and Mayer (2000), and Razin (2002).

6reflect the purchase (or liquidati on) of assets, at the macroecono mic level there is no simple way
to ascertain the extent to which they actually fi nance capital, rather than current, expenditures.
In the case of M&A, one can draw a further distinction between cross-border mergers ,
which occur when the assets and operation of fi rms from different countries are combined to
establish a new legal identity, and cross-border acquisitions , which occur when the control of
assets and operations is transferred from a local to a foreign company (with the former becoming
an affiliate of the latter).
In practice, world M&As have been predom inantly driven by acquisitions. Cross-border
mergers represented only 3 percent of cross-border M&As in 1999.10 Also, over 50 percent of
cross-border M&As in 1999 took the form of fu ll (or outright) acquisitio ns. Minority acquisitions
by foreign firms (that is, purchases of 10-49 per cent participation in total capital) represented
one-third of acquisitions in developing countries and less than 20 percent in developed countries
(see UNCTAD 2000).
Data on FDI inflows and outflows, as well as worldwide cross-border M&As, are
collected by UNCTAD’s World Investment Report (various years). We construct greenfield FDI
by subtracting cross-border M&As from FDI inflows. Our sample is dictated by the availability
of data on cross-border M&A transactions, wh ich is quite limited prior to 1987. Thus, the
analysis focuses on the period 1987-2001. It include s 72 countries, with a combined total of 848
country-year observations.
Tables 1 and 2 provide an overview of the major trends in the volume and composition of
FDI. Table 1 documents the changing patterns of external financing to in dustrial and developing

10 In reality, even when mergers are supposedly between rela tively equal partners, most are in fact acquisitions with
one company controlling the other.

7countries since the mid-1980s.11 Between 1987-89 and 2000-01, ne t financing to developing
countries rose from a negative 0.3% to 2% of the recipient economies’ GDP. This increase
reflected a parallel rise in ne t FDI flows by a similar magnitude. Over the same period, net FDI
flows went up from 0.5% to 2.5% of GDP. Net portfolio equity flows also rose, although by a
very modest amount, from virtually zero in th e late 1980s to some 0.1 percent of GDP in 2000-
2001. In turn, net debt flows to developing countries rose in th e early 1990s but then collapsed
following the East Asia and Russi a crises, becoming sharply negati ve by 2000-01. As a result of
these trends, in the latter year s net FDI exceeded total net financing flows to LDCs (see World
Bank 2001).
Table 1 also shows the figures for Latin Am erican countries, which were the primary
destination of the FDI boom of the 1990s. For these countries, total net flows rose from -2.2% to
3.1% of GDP between the late 1980s and the early 2000s . Over half of this increase took the form
of higher net FDI. In fact, increasing FDI between the first half of the 1990s and the early 2000s
more than made up for the collapse in all other flows over the same period.
Unlike with developing countries, net FDI fl ows to industrial economies showed little
change over the period under consideration. Clos er inspection reveals that both inflows and
outflows rose markedly, leaving the net difference virtually unchanged.
Table 2 offers a detailed breakdown of FDI fl ows over the same time period. In industrial
economies, almost all of the increase in inflow s took the form of higher cross-border M&A, of
which a very small portion was due to privatization of public enterprises. As a result, in industrial
countries M&A transactions were about 7 tim es larger than greenfield FDI in 2000-01.

11 Table 1 reveals that in our sample net flows to both industrial and developing countries do not add up to zero. This
is primarily due to the fact that our sample of countrie s is incomplete (especially concerning a few developing
economies that are international financial cen ters where large FDI outflows originate).

8 As for developing countries, three stylized facts emerge. First, compared with the sharp
rise in inflows, FDI outflows remain relatively modest. Although they have risen over the last
decade, in 2000-01 outflows amounted to less than a third of inflows in developing countries as a
whole, and even less (about 10 pe rcent) in Latin America. Thus , for developing countries FDI
inflows and net flows have moved in close tandem , in contrast with indu strial economies, where
large increases in inflows have translated into little change in net flows.
Second, a considerable portion of the rise in FDI inflows to developing countries over the
last decade took the form of increased cross-bord er M&A. By the early 2000s, these had grown to
account for nearly half (and even more in the ca se of Latin America) of FDI inflows, up from
about 10 percent in the late 1980s. Unlike in industrial countries, how ever, in developing
economies greenfield FDI still accounts for a large portion of FDI inflows.
Third, much of this M&A increase was due to privatization of public assets. The latter
accounted for roughly one-third of the increase in M&A inflows to developing countries in
general, and half for Latin America in partic ular, over the period during which comprehensive
privatization data are available.
3. Econometric analysis
Objective. Our empirical objective is to analyze the dynamic relationship between
greenfield FDI, cross-border M&A, domestic i nvestment, and GDP growth. Specifically, we
want to examine how the behavior of a given variable is related to the future behavior of the rest.
There are two aspects to this an alysis: effect and predictabilit y. The first deals with whether
changes in a variable have a lasting impact on another. Th e second examines whether the
behavior of a given variable helps predict the behavior of the rest.

9Methodology. Our methodology consists of estimatin g and testing bivariate vector
autoregressions (VAR) in a panel setting (tha t is, combining cross-country and time-series
observations). The VAR equations have the following form,
ti i t ti ti ti xLB yLA y, , , , )( )( εµη+++ + =
ti i t ti ti ti xLD yLC x, , , , )( )( υψφ+++ + =
where y and x represent the two variables of interest; L is the lag operator; A, B, C, and D are
vectors of coefficients; ηt and φt are unobserved time effects; µi and ψi are unobserved country
effects, and εi,t and νi,t are regression residuals. The subscripts i and t denote country and time,
respectively. As is standard in non-structural VAR analys is, we do not impose any cross-
equation parameter restrictions, we allow for a free cross-equation error covariance, and we
interpret each equation as a reduced-form regression.
We choose the optimal lag structure for th e panel VARs through like lihood ratio tests.
This turns out to be 4 or 5 la gs, depending on the specific biva riate system. To assess the
robustness of our results, we present the estima tion without country- and time-specific effects,
with only country effects, and with both country and time effects.
As stated above, we have two empirical objectives . First, we are interested in the impact
of changes in a variable, say x, on the other, say y. The direct impact of x on y, given the past
history of y, is given by the sum of th e coefficients on all lagged x. Using the properties of the
lag operator, this imp act would be equal to B(1). From estimation of the VAR, we can obtain the
point estimate of B(1) and, for the purpose of statistical in ference, its associated standard
deviation. From the estimated coefficients we can also obtain the long-run effect of x on y. The
long-run effect takes into account both the direct impact of x on y (given the past history of y) and

10the autoregressive properties of y (to account for own and cross fee dback effects). Provided that
y follows a stable process, the long-run effect of x on y is given by B(1)/[1-A(1)] .
Second, we want to examine whether a variable, say x, helps forecast the other variable in
the system, say y, beyond what the past history of y predicts. This is a test of Granger-causality,
and, in the example above, it amounts to testi ng if the coefficients of the lag polynomial B are
statistically significantly different from zero.
The two issues of interest –namely, impact and Granger-causality– are related but not
identical. There may be cases when a variable ha s predictive power for anot her, yet its impact is
zero because coefficients on differe nt lags cancel each other. Howe ver, in the relationships we
consider, it is usually th e case that when the impact is statisti cally zero, there is also no indication
of Granger causality.
Sample. Our full sample consists of annual inform ation for 72 countries during the period
1987-2001. The sample is divided into 22 industr ial and 50 developing countries. See Appendix
Table A for the complete list of countries included in the sample. We do not attempt to pool all
72 countries for estimation of a singl e set of coefficients because, as we discuss below, industrial
and developing countries exhibit different relati onships among the variables of interest. Given
the increasing importance of Latin America as a re cipient of FDI flows, we consider separate
estimation for the countries in this region.
Definitions. In the empirical analysis, we use the following definitions for the variables of
interest. Economic growth is the log difference of real GDP in consecutive years. Domestic
investment is equal to gross fixed capital formati on, expressed as a ratio to current GDP. Cross-
border mergers & acquisitions and greenfield FDI are expressed as ratios to GDP. Given that our
objective is to capture the effect of foreign participation in th e domestic economy, we consider

11inflows, rather than net fl ows, for both types of FDI. 12 See Appendix Table B for summary
statistics on all variables of interest.
Results. The estimation and inference results are summarized in Tables 3-7. For each
vector auto-regression, we report the sum of the coefficients on the lagged terms of each variable,
the p-value for the hypothesis that th is direct effect is not statisti cally significant, and the p-value
for the corresponding test that there is no Granger causality.
Table 3 examines the relationship between th e two types of FDI, that is, greenfield
investment and M&A. Tables 4 and 5 examine the link between domestic investment and, respectively, greenfield FDI and M&A FDI. Ta bles 6 and 7 study the re lationship between GDP
growth and the two types of FDI, respectivel y. Finally, Table 8 summarizes the results.
Before discussing the bivariate VAR results ta ble by table, we would like to examine the
inertial properties of our variables of intere st –that is, their dependence on their own past
realizations, given the past of th e other variable in the system. Comparing results across tables,
the following points arise. First, the autoregres sion coefficients drop considerably in all cases
once we account for country-speci fic effects. When the correct specifi cation of the dynamic
system includes country-specific effects, ignoring them in estimation leads to an upward bias in
the autoregression coefficients, in accordance with theoretical predictions (Robertson and
Symons 1992). This result reflects the correla tion between the unobserved country effects with
all (current and lagged) values of the variable of interest. Second, for all variables in all systems,
the sum of autoregression coefficients is statisti cally greater than zero and lower than one. That
is, all variables feature smooth positi ve persistence, not cyclical or explosive. Third, GDP growth

12As mentioned before, for developing countries the di stinction between inflows and net flows is largely
inconsequential. The same does not apply to industrial countries, however.

12is the least persistent of all variables considered here. The persistence (or inertia) of GDP growth
is lower in the samples of developing countries a nd Latin America (autoregression coefficients of
around 0.15) than in industri al countries (about 0.3).13 Fourth, in all samples the most persistent
variable is gross domestic inve stment (0.5 – 0.6), followed by greenfield and M&A FDI.
Greenfield FDI appears to be as persistent in i ndustrial as in developing countries (0.30 – 0.55,
depending on the bivariate system). However, M&A FDI is somewhat more persistent in
industrial countries (0.5) than in developing eco nomies (0.25). Note than greenfield and M&A
FDI in developing countries are not memory-less processes, as is usually implied in claims that
the booming cross-border investment to emergi ng countries is only the result of a one-shot
privatization process. Apart from the nuances just noted, greenfield a nd M&A FDI have similar
autocorrelation characteristics. As explained below, this is the first of many similarities between
the two types of FDI regarding th eir dynamic properties.
Table 3 indicates that in the samples of industrial, developing, and Latin American
countries, higher M&A leads to more greenfield FD I. For developing countries, in addition, an
increase in greenfield FDI leads to a rise in M&A FDI.14 These results are robust to the inclusion
of country- and time-specific effect s. In order to assess the econom ic importance of our results,
we can examine the size of long-run effects, as e xplained above. Using the point estimates of the
regression that controls for country- and time-specifi c effects, the long-run e ffect of a unit change
in M&A FDI on greenfield FDI is 0.97 for industr ial countries, 1.56 for de veloping countries, and
0.77 for Latin America. Thus, th is effect is similar in industrial and Lati n American countries,
but significantly larger in the full sample of devel oping countries. In additi on, in the latter group

13 In a different context, growth’s low persistence was also noted by Easterly, Kremer, Pritchett, and Summers
(1993).
14 As implied from the discussion below, this is the only instance of bi-directional VAR effects in the paper.

13there is feedback from greenfield FDI to M&A FDI, with a long-run multiplier of 0.20.15 The
implication of these results, both qualitative and quantitatively, is a strong connection between
both types of FDI. In particul ar, we can conclude that FDI in itially driven by the purchase of
existing companies results in fresh investment in the following years. In industrial countries this
subsequent rise is similar in magnitude to the initial investment. In developing countries, the
result is even more optimistic because the gain in greenfield FDI largely surpasses the original capital purchase. This implies that the end of the privatization process in Latin America and
other parts of the world need not dry up FDI but may instead give way to rising greenfield
investment.
In Table 4, we study the rela tionship between domestic inve stment and greenfield FDI.
The main qualitative result is the same for indust rial, developing, and Latin American countries.
That is, in all samples, greenfield FDI appear s to precede domestic investment, but not the
reverse. The inclusion of time- or country-specific eff ects does not change the substance of this
result. Quantitatively, the long-r un effect of greenfield FDI on domestic investment is more than
twice larger in developing and Latin American countries (with multiplie rs of 0.73 and 0.65,
respectively) than in industrial countries (0.29).
16
Table 5 presents the results of the link betw een domestic investment and M&A FDI. The
basic result is common to all samples and similar to the case of greenfield FDI. That is, M&A FDI leads to a rise in domestic investment, but th e reverse effect is not statistically significant
(although there is some evidence of predictability from domestic i nvestment to M&A in industrial
countries). Quantitatively, however, there are some differences of degree between the two types

15 In what follows in the text, we label “long-run multiplier” the magnitude of the long-run effect of a unit change in
a given variable on another.

14of FDI. First, in general the multipliers of domestic investment with respect to M&A FDI are
larger than with respect to greenfield FDI. S econd, the multipliers of domestic investment to
M&A FDI are higher in industrial (1 .05) than in devel oping and Latin American countries (0.85
and 0.80, respectively); this is just the opposite to what ha ppened with greenfield FDI.
The positive effect of either type of FDI on do mestic investment is encouraging. On the
other hand, it may appear surprising that domestic i nvestment does not lead to a rise in either type
of FDI. On second inspection, however, we ca n find arguments in opposite directions that, in
practice, would cancel each other. For instance, an increase in domestic investment may lead to
more FDI if it serves as an in dication that there are profitable opportunities to be exploited in the
country. On the other hand, if domestic investment decreases – for instance, because of rising
liquidity or solvency problems in local firms – FDI inflows may increase in the following years
to take advantage of idle opportuni ties and, thus, partially fill th e gap left by resident investors.
In Table 6 we examine the re lationship between economic gr owth and greenfield FDI.
The main result is qualitatively the same in all samples. Economic growth appears to precede and
produce a positive impact on greenfield FDI. However, there appears to be no statistically significant reverse effect from greenfield FDI to economic growth. The impact of growth on
greenfield investment is larger for industrial than de veloping or Latin Amer ican countries, with
long-run multipliers of 0.40, 0.21, and 0.26, respectively.
Finally, Table 7 presents the results on the links between economic growth and M&A
FDI. Again, the basic result is the same for all samples. An in crease in economic growth leads to
a rise in M&A, but the reverse is not statistically significant. As in the case of greenfield FDI, the

16 In these and other calculations of long-run effects, we use the point estimates obtained in the regressions that
control for country and time specific effects.

15response of M&A FDI to changes in economic growth is larger in industr ial countries (multiplier
of 0.65) than in developing (0.27) or Latin American countries (0.21).
The asymmetric relationship between FDI and economic growth deserves further
discussion. The fact that either type of FDI does not l ead to larger growth may indicate that FDI
simply cannot account for the majority or the most important of the many determinants of
economic growth.17 Furthermore, it is likely that th e relationship between FDI and growth
depends largely on third f actors driving both variables. For in stance, in countries where FDI rises
as result of higher import tariffs, we shoul d expect a negative relationship between FDI and
economic growth. The opposite would occur when FDI rises because of an improvement in
public infrastructure and government institutions.18 On the other hand, GDP growth can capture
FDI’s most relevant determinants. Given that economic growth is arguably the most important
sign of profitable investment opportunities in a c ountry, it can serve as a strong pull factor for
FDI.19
4. Concluding remarks
In the last 15 years, FDI has become the predominant form of external financing in
developing countries, far surpassi ng traditional sovereign borrowing. To be sure, the growth of
FDI is part of a more general trend in develo ping countries consisting of a rapid expansion of
private capital flows and contracti on of official ones. In industri al countries, FD I has grown more
than any other type of capital flow, although it still ranks second to foreign borrowing.
Not only has total FDI grown in importance, but also its composition has experienced a
remarkable change over the last 15 years. In developing countries, the share of cross-border
mergers and acquisitions in FDI was about 10% in the mid 1980s and increased to more than a

17 See Carkovic and Levine (2002) for similar results.

16third at the beginning of the 2000s . The lion’s share of the in crease in cross-border M&A is
explained by the privatization of state enterp rises that took place during the 1990s in many
developing countries. The shar e of cross-border M&A in FDI also increased markedly in
industrial countries. In that context, this paper set out to answer two questions. The first one is about the continuation of the FDI boom to developing countri es; specifically, would it continue after the
privatization process and the ensuing expans ion of cross-border M&A had dried up? Our
approach to this question cons isted in evaluating to what extent greenfield FDI (that is,
investment in new assets) would follow an in crease in cross-border M&A (the purchase of
existing assets). For this purpose, we estimated bivariate vector auto regressions in a pooled
cross-country, time-series setting. We worked with annual data for the period 1987-2001 for
samples of 22 industrial and 50 developing countries. Table 8 pr ovides a summary of results.
We found that an expansion of M&A is indeed followed by an increase in greenfield FDI.
According to our estimates, an increase in M&A by 1 percent of GDP leads to a rise in greenfield
FDI by about 1 and 1.5 percentage points of GDP in industrial and developing countries,
respectively. That is, the subseque nt expansion of greenfield FDI is at least as large as the initial
increase in M&A, and substantially more in de veloping economies. Therefore, if the experience
of the 1990s and late 1980s is a good predictor for the future, an expansion of greenfield FDI will ensure that the FDI boom will con tinue in the future even after the privatization process has
stopped. The second question we wanted to address concerns the cau sality (in the sense of time
precedence) between the two forms of FDI and domestic investment and economic growth.

18 See Stein and Daude (2001) and Alfaro et al. (2002) for related discussions.

17Using the afore-mentioned bivariate VAR met hodology on the same panel of countries and time-
series observations, we find that both greenfield and M&A FDI lead domestic investment but are
led by GDP growth. Therefore, economic growth, as the most important indicator of domestic
rates of return, serves as an effective “pull” factor for foreign investment; and in turn, FDI helps increase domestic investment in the future.
In order to close the virtuous circle between FDI, domes tic investment, and growth, it
would be necessary for investment to lead to ec onomic growth. This important link is not the
subject of this paper; however, using a methodol ogy similar to this paper’s VAR systems, we
have examined the dynamic relationship between domestic investment and economic growth in
our sample.
20 We confirm the results obtained by Bl omstrom, Lipsey, a nd Zejan (1996) and
Attanasio, Scorcu, and Picci (2000) in the sense that while growth causes investment, investment
does not lead to growth. Whether this is a re flection of poor-quality in vestment (see Pritchett
2000) or the fact that economic growth depends on a multitude of factors that cannot be fully
captured by developments in FDI or domestic in vestment (see Barro and Sala-i-Martin 1995, p.
433) is a subject for further research.

19 See Calderón, Loayza, and Servén (2001) and Albuquerque, Loayza and Servén (2003).
20 These results are not presented in th e paper but are available on request.

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21
Table 1
FDI and Other Capital Flows
(As a percentage of GDP, weighted averages)
Foreign Direct Investment Portfolio Equity Loan Total Total
Inflows Outflows Inflows Outflows Inflows Outflows Net FDI Net Inflows
INDUSTRIAL COUNTRIES
1987-89 0.99% 1.27% 0.28% 0.32% 4.88% 3.66% -0.28% 0.90%1990-94 0.76% 1.17% 0.50% 0.51% 3.37% 2.52% -0.41% 0.43%
1995-99 1.74% 2.33% 1.29% 1.27% 5.77% 4.90% -0.60% 0.30%
2000-01 3.67% 3.86% 2.37% 2.36% 5.27% 4.27% -0.19% 0.83%
DEVELOPING COUNTRIES
1987-89 0.86% 0.40% 0.02% 0.04% -0.51% 0.23% 0.46% -0.29%
1990-94 1.43% 0.65% 0.49% 0.13% 1.75% 0.79% 0.79% 2.11%1995-99 2.80% 0.97% 0.64% 0.38% 1.61% 1.63% 1.83% 2.08%2000-01 3.63% 1.10% 0.76% 0.63% 0.15% 0.84% 2.53% 1.97%
LATIN AMERICAN COUNTRIES
1987-89 0.74% 0.10% 0.03% 0.00% -3.14% -0.28% 0.64% -2.19%
1990-94 1.15% 0.23% 0.85% 0.05% 2.01% 1.04% 0.93% 2.69%
1995-99 3.21% 0.49% 0.26% 0.13% 1.40% 0.98% 2.72% 3.27%2000-01 3.78% 0.40% 0.05% 0.19% 0.08% 0.23% 3.38% 3.08%
Source: Authors' elaboration from IMF data on balance of payments flows.

22
Table 2
FDI, Greenfield Investm ent and M &As
(As a percentage of GDP, weighted averages)
Net FDI FDI Inflows FDI
Flows Total Greenfield M&A Total M&A Privatization Outflows
INDUSTRIAL COUNTRIES
1987-89 -0.28% 0.99% 0.23% 0.76% 0.01% 1.27%
1990-94 -0.41% 0.76% 0.26% 0.50% 0.02% 1.17%
1995-99 -0.60% 1.74% 0.26% 1.48% 0.06% 2.33%
2000-01 -0.19% 3.67% 0.46% 3.21% n.a. 3.86%
DEVELOPING COUNTRIES
1987-89 0.46% 0.86% 0.77% 0.09% 0.01% 0.40%
1990-94 0.79% 1.43% 1.14% 0.30% 0.08% 0.65%
1995-99 1.83% 2.80% 1.87% 0.93% 0.31% 0.97%
2000-01 2.53% 3.63% 2.10% 1.53% n.a. 1.10%
LATIN AMERICAN COUNTRIES
1987-89 0.64% 0.74% 0.65% 0.08% 0.01% 0.10%
1990-94 0.93% 1.15% 0.68% 0.47% 0.20% 0.23%
1995-99 2.72% 3.21% 1.58% 1.63% 0.74% 0.49%
2000-01 3.38% 3.78% 1.82% 1.97% n.a. 0.40%
Source: Authors' elaboration from UNCTAD data on FDI flows and cross-border mergers and acquisitions (M&As).

23
Table 3
Dynamic Relationship between Greenfield FDI Inflows (GrFDI) and Cross-Border M ergers and Acquisitions (M &As): Causality Tests
72 countries, Annual Data for the 1987-2001 period
Industrial Countries Developing Countries Latin America
To: To: To: To: To: To:
GrFDI M&As GrFDI M&As GrFDI M&As
OLS Estimation
– From GrFDI: Sum Coeff. 0.5379 0.0745 0.7430 0.1230 0.6631 0.0824
[p-value] (0.024) (0.506) (0.000) (0.002) (0.000) (0.388)
Causality [p-value] (0.005) (0.784) (0.000) (0.018) (0.000) (0.703)
– From M&As: Sum Coeff. 0.5322 0.9193 0.6679 0.3351 0.4013 0.4732
[p-value] (0.026) (0.000) (0.000) (0.000) (0.030) (0.002)
Causality [p-value] (0.032) (0.000) (0.000) (0.002) (0.007) (0.001)
Time Effects
– From GrFDI: Sum Coeff. 0.5201 0.1709 0.7645 0.0992 0.6887 0.0654
[p-value] (0.029) (0.111) (0.000) (0.011) (0.000) (0.769)
Causality [p-value] (0.000) (0.310) (0.000) (0.054) (0.000) (0.914)
– From M&As: Sum Coeff. 0.5580 0.8665 0.7692 0.2392 0.4351 0.3980
[p-value] (0.037) (0.000) (0.000) (0.002) (0.000) (0.001)
Causality [p-value] (0.000) (0.000) (0.000) (0.013) (0.000) (0.001)
Country Effects
– From GrFDI: Sum Coeff. 0.4614 0.0687 0.5591 0.1341 0.4175 0.1325
[p-value] (0.095) (0.624) (0.000) (0.022) (0.000) (0.174)
Causality [p-value] (0.000) (0.532) (0.000) (0.086) (0.000) (0.561)
– From M&As: Sum Coeff. 0.6278 0.5339 0.7181 0.2535 0.5220 0.2616
[p-value] (0.054) (0.001) (0.000) (0.005) (0.000) (0.029)
Causality [p-value] (0.000) (0.000) (0.000) (0.009) (0.000) (0.033)
Country & Time Effects
– From GrFDI: Sum Coeff. 0.4204 0.1049 0.5585 0.1469 0.3850 0.0735
[p-value] (0.047) (0.412) (0.000) (0.010) (0.000) (0.756)
Causality [p-value] (0.000) (0.797) (0.000) (0.091) (0.002) (0.943)
– From M&As: Sum Coeff. 0.5611 0.4870 0.6894 0.2509 0.4795 0.2537
[p-value] (0.005) (0.007) (0.003) (0.006) (0.000) (0.018)
Causality [p-value] (0.000) (0.049) (0.000) (0.010) (0.000) (0.008)
No. Countries 22 22 50 50 18 18
No. Observations 263 263 585 585 216 216
Source: Authors' calculations.

24 Table 4
Dynamic Relationship between Domestic Investment (GDI) and Greenfield Investment (GrFDI): Causality Tests
72 countries, Annual Data for the 1987-2001 period
Industrial Countries Developing Countries Latin America
T o :T o : T o :T o : T o :T o :
GrFDI GDI GrFDI GDI GrFDI GDI
OLS Estimation
– From GrFDI: Sum Coeff. 0.7264 0.0665 0.6253 0.2420 0.5731 0.1025
[p-value] (0.035) (0.052) (0.000) (0.017) (0.000) (0.036)
Causality [p-value] (0.023) (0.013) (0.000) (0.010) (0.000) (0.041)
– From GDI: Sum Coeff. -0.1391 0.8846 0.0318 0.8824 0.0352 0.9217
[p-value] (0.267) (0.000) (0.110) (0.000) (0.375) (0.000)
Causality [p-value] (0.153) (0.000) (0.319) (0.000) (0.334) (0.000)
Time Effects
– From GrFDI: Sum Coeff. 0.7365 0.0728 0.6300 0.2683 0.5996 0.1238
[p-value] (0.011) (0.033) (0.000) (0.001) (0.005) (0.108)
Causality [p-value] (0.000) (0.045) (0.000) (0.000) (0.034) (0.032)
– From GDI: Sum Coeff. -0.1128 0.6347 0.0285 0.8854 0.0144 0.9101
[p-value] (0.251) (0.000) (0.108) (0.000) (0.272) (0.000)
Causality [p-value] (0.711) (0.000) (0.394) (0.000) (0.631) (0.000)
Country Effects
– From GrFDI: Sum Coeff. 0.3728 0.1293 0.2963 0.3229 0.3321 0.3847
[p-value] (0.029) (0.028) (0.000) (0.002) (0.002) (0.010)
Causality [p-value] (0.000) (0.037) (0.000) (0.000) (0.005) (0.012)
– From GDI: Sum Coeff. -0.2030 0.5224 0.0461 0.5074 0.0670 0.5290
[p-value] (0.271) (0.000) (0.244) (0.000) (0.220) (0.000)
Causality [p-value] (0.587) (0.000) (0.641) (0.000) (0.294) (0.000)
Country & Time Effects
– From GrFDI: Sum Coeff. 0.3726 0.1213 0.2961 0.3827 0.3415 0.3070
[p-value] (0.024) (0.041) (0.000) (0.000) (0.000) (0.009)
Causality [p-value] (0.000) (0.025) (0.000) (0.000) (0.000) (0.006)
– From GDI: Sum Coeff. -0.0248 0.5798 0.0282 0.4786 0.0295 0.5241
[p-value] (0.367) (0.000) (0.500) (0.000) (0.317) (0.000)
Causality [p-value] (0.422) (0.000) (0.925) (0.000) (0.432) (0.000)
No. Countries 22 22 50 50 18 18
No. Observations 221 221 578 578 204 204
Source: Authors' calculations.

25
Table 5
Dynamic Relationship between Domestic Investment (GDI) and Cross-Border Mergers and Acquisitions (M&As): Causality Tests
72 countries, Annual Data for the 1987-2001 period
Industrial Countries Developing Countries Latin America
T o :T o : T o :T o : T o :T o :
M&As GDI M&As GDI M&As GDI
OLS Estimation
– From M&As: Sum Coeff. 0.8208 0.1092 0.2996 0.2381 0.4752 0.1490
[p-value] (0.000) (0.009) (0.005) (0.022) (0.000) (0.017)
Causality [p-value] (0.000) (0.031) (0.034) (0.032) (0.003) (0.029)
– From GDI: Sum Coeff. -0.0942 0.9011 0.0144 0.9101 0.0315 0.9342
[p-value] (0.143) (0.000) (0.272) (0.000) (0.292) (0.000)
Causality [p-value] (0.054) (0.000) (0.631) (0.000) (0.350) (0.000)
Time Effects
– From M&As: Sum Coeff. 0.7446 0.3607 0.2106 0.2295 0.3937 0.1438
[p-value] (0.000) (0.021) (0.005) (0.003) (0.001) (0.040)
Causality [p-value] (0.000) (0.009) (0.024) (0.000) (0.000) (0.047)
– From GDI: Sum Coeff. -0.0626 0.9360 0.0111 0.9145 0.0176 0.9550
[p-value] (0.387) (0.000) (0.330) (0.000) (0.472) (0.000)
Causality [p-value] (0.129) (0.000) (0.682) (0.000) (0.309) (0.000)
Country Effects
– From M&As: Sum Coeff. 0.4372 0.4082 0.2664 0.4360 0.2273 0.3490
[p-value] (0.000) (0.000) (0.023) (0.005) (0.060) (0.034)
Causality [p-value] (0.011) (0.003) (0.011) (0.000) (0.041) (0.021)
– From GDI: Sum Coeff. -0.1774 0.5656 0.0266 0.5808 0.0807 0.5687
[p-value] (0.234) (0.000) (0.176) (0.000) (0.096) (0.000)
Causality [p-value] (0.040) (0.000) (0.290) (0.000) (0.161) (0.000)
Country & Time Effects
– From M&As: Sum Coeff. 0.4855 0.4781 0.2743 0.3702 0.2481 0.3536
[p-value] (0.017) (0.007) (0.002) (0.002) (0.038) (0.004)
Causality [p-value] (0.018) (0.026) (0.016) (0.000) (0.032) (0.013)
– From GDI: Sum Coeff. -0.1333 0.5468 0.0455 0.5641 0.0194 0.5582
[p-value] (0.130) (0.000) (0.180) (0.000) (0.706) (0.000)
Causality [p-value] (0.010) (0.000) (0.390) (0.000) (0.383) (0.000)
No. Countries 22 22 50 50 18 18
No. Observations 221 221 578 578 204 204
Source: Authors' calculations.

26
Table 6
Dynamic Relationship between Economic Growth and Greenfield Investment (GrFDI): Causality Tests
72 countries, Annual Data for the 1987-2001 period
Industrial Countries Developing Countries Latin America
To: To: To: To: To: To:
GrFDI Growth GrFDI Growth GrFDI Growth
OLS Estimation
– From GrFDI: Sum Coeff. 0.5138 -0.0685 0.6483 0.0920 0.6399 0.1112
[p-value] (0.052) (0.386) (0.000) (0.726) (0.000) (0.256)
Causality [p-value] (0.046) (0.726) (0.000) (0.828) (0.000) (0.438)
– From Growth: Sum Coeff. 0.2634 0.5196 0.1744 0.3867 0.1809 0.2864
[p-value] (0.029) (0.000) (0.016) (0.000) (0.025) (0.030)
Causality [p-value] (0.023) (0.000) (0.029) (0.000) (0.022) (0.000)
Time Effects
– From GrFDI: Sum Coeff. 0.5330 -0.0972 0.6512 0.0925 0.6029 0.1152
[p-value] (0.040) (0.219) (0.000) (0.621) (0.000) (0.917)
Causality [p-value] (0.000) (0.444) (0.000) (0.931) (0.000) (0.978)
– From Growth: Sum Coeff. 0.2404 0.6298 0.1784 0.3868 0.1910 0.2824
[p-value] (0.020) (0.000) (0.029) (0.000) (0.028) (0.035)
Causality [p-value] (0.028) (0.000) (0.031) (0.000) (0.028) (0.001)
Country Effects
– From GrFDI: Sum Coeff. 0.2624 -0.1547 0.3046 0.1309 0.4021 0.0915
[p-value] (0.014) (0.110) (0.000) (0.321) (0.000) (0.238)
Causality [p-value] (0.000) (0.368) (0.000) (0.735) (0.000) (0.598)
– From Growth: Sum Coeff. 0.2405 0.2414 0.1528 0.1866 0.1511 0.1486
[p-value] (0.024) (0.001) (0.031) (0.029) (0.020) (0.040)
Causality [p-value] (0.033) (0.000) (0.032) (0.011) (0.024) (0.003)
Country & Time Effects
– From GrFDI: Sum Coeff. 0.2526 -0.1673 0.3008 0.1207 0.4251 0.0999
[p-value] (0.010) (0.105) (0.000) (0.433) (0.019) (0.585)
Causality [p-value] (0.000) (0.173) (0.000) (0.857) (0.011) (0.939)
– From Growth: Sum Coeff. 0.1772 0.3503 0.1467 0.1778 0.1505 0.1214
[p-value] (0.025) (0.000) (0.009) (0.021) (0.006) (0.010)
Causality [p-value] (0.026) (0.000) (0.002) (0.007) (0.005) (0.015)
No. Countries 22 22 50 50 18 18
No. Observations 252 252 585 585 216 216
Source: Authors' calculations.

27 Table 7
Dynamic Relationship between Economic Growth and Cross-Border Mergers and Acquisitions (M&As): Causality Tests
72 countries, Annual Data for the 1987-2001 period
Industrial Countries Developing Countries Latin America
To: To: To: To: To: To:
M&As Growth M&As Growth M&As Growth
OLS Estimation
– From M&As: Sum Coeff. 0.8952 0.0144 0.3025 -0.1621 0.4686 -0.1114
[p-value] (0.000) (0.863) (0.004) (0.347) (0.001) (0.412)
Causality [p-value] (0.000) (0.524) (0.029) (0.239) (0.004) (0.199)
– From Growth: Sum Coeff. 0.1145 0.5183 0.1080 0.3933 0.1097 0.1760
[p-value] (0.029) (0.000) (0.042) (0.000) (0.031) (0.020)
Causality [p-value] (0.023) (0.000) (0.046) (0.000) (0.039) (0.000)
Time Effects
– From M&As: Sum Coeff. 0.8122 0.0760 0.3121 -0.1294 0.3932 -0.1030
[p-value] (0.000) (0.398) (0.005) (0.316) (0.001) (0.877)
Causality [p-value] (0.000) (0.543) (0.025) (0.473) (0.001) (0.322)
– From Growth: Sum Coeff. 0.3330 0.6178 0.2004 0.3960 0.2047 0.1742
[p-value] (0.044) (0.000) (0.047) (0.000) (0.035) (0.042)
Causality [p-value] (0.032) (0.000) (0.020) (0.000) (0.026) (0.001)
Country Effects
– From M&As: Sum Coeff. 0.4699 0.1068 0.2033 -0.1142 0.2575 -0.2286
[p-value] (0.002) (0.394) (0.035) (0.418) (0.029) (0.263)
Causality [p-value] (0.000) (0.675) (0.027) (0.707) (0.039) (0.355)
– From Growth: Sum Coeff. 0.2580 0.2574 0.2305 0.1849 0.2088 0.1485
[p-value] (0.005) (0.001) (0.025) (0.021) (0.010) (0.030)
Causality [p-value] (0.030) (0.000) (0.020) (0.010) (0.013) (0.003)
Country & Time Effects
– From M&As: Sum Coeff. 0.5017 0.1909 0.2614 -0.0828 0.2481 -0.0818
[p-value] (0.020) (0.152) (0.004) (0.596) (0.024) (0.730)
Causality [p-value] (0.032) (0.342) (0.025) (0.659) (0.021) (0.405)
– From Growth: Sum Coeff. 0.3249 0.3292 0.2001 0.1308 0.1612 0.1424
[p-value] (0.029) (0.000) (0.011) (0.016) (0.028) (0.005)
Causality [p-value] (0.046) (0.000) (0.010) (0.007) (0.020) (0.010)
No. Countries 22 22 50 50 18 18
No. Observations 252 252 585 585 216 216
Source: Authors' calculations.

28 Table 8
Summary of Results
Industrial Countries Developing Countries Latin America
From Greenfield FDI to M&As .+ .
From M&As to Greenfield FDI ++ +
From Greenfield FDI to Domestic Investment ++ +
From Domestic Investment to Greenfield FDI .. .
From M&As to Domestic Investment ++ +
From Domestic Investment to M&As .. .
From Greenfield FDI to Economic Growth .. .
From Economic Growth to Greenfield FDI ++ +
From M&As to Economic Growth .. .
From Economic Growth to M&As ++ +
Note: "." represents no significant effect and "+", a statistically positive effect.

29 APPENDIX
Table A. Sample of Countries
No. Country Name Region No. Country Name Region
1 Argentina AMER 37 Jamaica AMER2 Australia IND 38 Jordan MENA
3 Austria IND 39 Japan IND4 Belgium IND 40 Kenya SSA
5 Bolivia AMER 41 Korea, Rep. Of EAP
6 Brazil AMER 42 Sri Lanka SA7 Botswana SSA 43 Morocco MENA
8 Canada IND 44 Madagascar SSA
9 Switzerland IND 45 Mexico AMER10 Chile AMER 46 Mali SSA
11 China EAP 47 Mauritius SSA
12 Cote d'Ivoire SSA 48 Malaysia EAP13 Colombia AMER 49 Nigeria SSA
14 Cape Verde SSA 50 Netherlands IND
15 Costa Rica AMER 51 Norway IND16 Germany IND 52 New Zealand IND
17 Denmark IND 53 Pakistan SA
18 Dominican Republic AMER 54 Panama AMER19 Ecuador AMER 55 Peru AMER
20 Egypt MENA 56 Philippines EAP
21 Spain IND 57 Portugal IND22 Finland IND 58 Paraguay AMER
23 France IND 59 Saudi Arabia MENA
24 United Kingdom IND 60 Senegal SSA25 Ghana SSA 61 Singapore EAP
26 Guinea SSA 62 El Salvador AMER
27 Greece IND 63 Sweden IND28 Guatemala AMER 64 Swaziland SSA
29 Hong Kong EAP 65 Thailand EAP
30 Honduras AMER 66 Tunisia MENA31 Indonesia EAP 67 Turkey MENA
32 India SA 68 Taiwan EAP
33 Ireland IND 69 Uruguay AMER34 Iceland IND 70 United States IND
35 Israel MENA 71 Venezuela AMER
36 Italy IND 72 South Africa SSA

30 Table B. Summary Statistics
FDI Gross Greenfield Cross-Border Gross Domestic Economic
Inflows FDI M&As Investment Growth
Industrial Mean 0.0310 0.0141 0.0169 0.2090 0.0207
Median 0.0161 0.0056 0.0078 0.2059 0.0206Std. Dev. 0.0689 0.0621 0.0259 0.0388 0.0241
Nobs. 263 263 263 263 263
Developing Mean 0.0213 0.0148 0.0065 0.2318 0.0177
Median 0.0125 0.0093 0.0003 0.2260 0.0186
Std. Dev. 0.0297 0.0303 0.0181 0.0749 0.0412
Nobs. 585 585 585 585 585
Latin America Mean 0.0249 0.0160 0.0089 0.2130 0.0115
Median 0.0187 0.0126 0.0012 0.2100 0.0138
Std. Dev. 0.0271 0.0233 0.0180 0.0570 0.0399
Nobs. 216 216 216 216 216