metadata: authors: 0: author: Burger, Martijn J.
1: author: Ianchovichina,Elena
count: World
docna: 0: docna: Surges and stops in FDI flows to developing
            countries : does the mode of entry make a difference ?
docty: Policy Research Working Paper
subtopic: Investment and Investment Climate,Financial Crisis Management & Restructuring,Coastal and Marine Resources,Economic Growth,Energy and Natural Resources,Global Environment
teratopic: Poverty Reduction,Macroeconomics and Economic Growth,Finance and Financial Sector Development,Private Sector Development
abstracts: cdata!: This paper investigates the factors
            associated with foreign direct investment "surges"
            and "stops," defined as sharp increases and
            decreases, respectively, of gross foreign direct investment
            inflows to the developing world and differentiated based on
            whether these events are led by waves in greenfield
            investments or mergers and acquisitions. Greenfield-led
            surges and stops occur more frequently than mergers and
            acquisitions-led ones and different factors are associated
            with the onset of the two types of events. Global liquidity
            is the only factor significantly associated with a surge,
            regardless of its kind, while decline in global economic
            growth and a surge in the preceding year are the only
            predictors of a stop. Greenfield-led surges and stops are
            more likely in low-income and resource-rich countries than
            elsewhere. Global growth, financial openness, and domestic
            economic and financial instability enable mergers and
            acquisitions-led surges. These results differ from those in
            the literature on surges and stops and are particularly
            relevant in countries where foreign direct investments
            dominate capital flows.
repnme: repnme: Surges and stops in FDI flows to developing
            countries : does the mode of entry make a difference ?
display_title: Surges and stops in FDI flows to
            developing countries : does the mode of entry make a difference
listing_relative_url: /research/2014/02/18912430/surges-stops-fdi-flows-developing-countries-mode-entry-make-difference
topicv3: Natural Resources m1326446 1979,Monetary and Financial Stability m1327169 1098,Environment, Natural Resources and Blue
            Economy m1327391 566,Investment Entry m1504647 2544,Mergers and Acquisitions m1505068 3872
publishtoextweb_dt: 2014-02-01T00:00:00Z
historic_topic: Poverty Reduction,Macroeconomics and Economic Growth,Finance and Financial Sector Development,Private Sector Development
no_of_pages: 57
author_keys: 0: author_key: 453205
model_extractions: WPS6771 Policy Research Working Paper 6771 Surges and Stops in FDI Flows to Developing Countries Does the Mode of Entry Make a Difference?

_Martijn J. Burger Elena I. Ianchovichina_ The World Bank Middle East and North Africa Region Office of the Chief Economist February 2014
Policy Research Working Paper 6771

# **Abstract**

This paper investigates the factors associated with foreign direct investment “surges” and “stops,” defined as sharp increases and decreases, respectively, of gross foreign direct investment inflows to the developing world and differentiated based on whether these events are led by waves in greenfield investments or mergers and acquisitions. Greenfield-led surges and stops occur more frequently than mergers and acquisitions-led ones and different factors are associated with the onset of the two types of events. Global liquidity is the only factor significantly associated with a surge, regardless of its kind, while decline in global economic growth and a surge in the preceding year are the only predictors of a stop. Greenfield-led surges and stops are more likely in low-income and resource-rich countries than elsewhere. Global growth, financial openness, and domestic economic and financial instability enable mergers and acquisitions-led surges. These results differ from those in the literature on surges and stops and are particularly relevant in countries where foreign direct investments dominate capital flows.

This paper is a product of the Office of the Chief Economist, Middle East and North Africa Region. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at eianchovichina@worldbank.org or mburger@ese.eur.nl.

_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 get 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 views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# **Surges and Stops in FDI Flows to Developing Countries: Does the Mode of Entry Make a Difference?**<sup>∗</sup>

**Martijn J. Burger**<sup>♦</sup> **and Elena I. Ianchovichina**<sup>♣</sup>

**Key words** : foreign direct investment (FDI), mode of entry, greenfield (GF) investment, mergers and acquisitions (M&A), capital flows, surges, stops, developing countries

**JEL codes** : F21, F23, F43, F44

**Sector Board:** EPOL

> ∗ We would like to thank Sergio Schmukler for providing extensive comments on several drafts of this paper and Bob Rijkers for his useful suggestions on an early version of this paper. The research was supported by funding through the World Bank’s Knowledge for Change Program. The findings, interpretations, and conclusions expressed in this paper are entirely ours and should not be attributed to the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent.

> ♦ Martijn J. Burger is assistant professor at the Department of Applied Economics, Erasmus University Rotterdam, Tinbergen Institute and Erasmus Research Institute of Management (ERIM), P.O. Box 1738, 3000 DR Rotterdam, the Netherlands. Tel: +31 (0)10 4089579. Fax: +31 (0)10 4089141. E-mail: <u>mburger@ese.eur.nl. URL: http://www.mjburger.net.</u>

> ♣ Elena Ianchovichina is lead economist at the Chief Economist Office, Middle East and North Africa Region, World Bank, 1818 H Street NW, Washington, DC 20433, USA, Tel: +1 202 458 8910, E-mail: <u>eianchovichina@worldbank.org .</u>
# **1. Introduction**

Over the past three decades the world has witnessed a dramatic rise in foreign direct investment (FDI) flows. Prior to 1985 the growth rate of FDI flows was comparable to the growth rates of world trade and output, but after that FDI flows grew at a much faster pace than either world trade or world output. The growing importance of FDI flows has spurred burgeoning literatures on the causes and effects of FDI in international and financial economics, international business, and economic geography.<sup>1</sup> Still, the rise of FDI has not proceeded in a smooth fashion. Since the 1980s, there have been distinct waves of FDI with corresponding surges and stops, especially in developed countries (Andrade et al., 2001). Although developed countries have generally received more FDI flows than developing ones and host the majority of the inward FDI stock, developing countries have caught up (UNCTAD, 2011). In 2010, for the first time the developing world received more FDI flows than their developed counterparts, but some developing countries were more successful in attracting FDI than others. The distribution of inward FDI flows has been persistently disproportionate in that investments have concentrated within a limited number of developing countries (see also Noorbakhsh et al., 2001; UNCTAD, 2011).

This paper investigates the nature and determinants of FDI “stops” and “surges”, defined as sharp increases and decreases, respectively, of gross FDI flows<sup>2</sup> to the developing world, initiated by foreign investors and differentiated based on whether these events are led by waves in greenfield investments (GF) or mergers and acquisitions

> 1 See Barba Navaretti and Venables (2004), Blonigen (2005), and Brakman et al. (2006) for overviews of these literatures.

> 2 These flows include equity capital, reinvestment of earnings, other long-term capital, and short-term capital less disinvestments.

2
(M&A). In particular, we explore whether the mode of entry affects the incidence and determinants of FDI surges and stops. Several studies compare different types of financial flow events (e.g., Sula, 2010; Cardarelli et al., 2010; Agosin and Huaita, 2012; Forbes and Warnock, 2012; Furceri et al., 2012; Ghosh et al., 2012), but to our knowledge, none looks separately at the incidence and determinants of GF-led and M&A-led stops and surges in FDI flows to the developing world. The distinction is important for a number of reasons. The two types of FDI flows occur for different reasons and have different effects, characteristics, and incidence.<sup>3</sup> GF investment inflows finance the construction of new facilities which augment the stock of physical capital and thus expand the production capacity in countries, increasing market competition and employment (Mattoo et al., 2004). M&As predominantly involve a change in ownership via the purchase of existing assets, although they might result in a more efficient allocation of resources (Kim, 2009; Wang and Wong, 2009; Harms and Méon, 2012).<sup>4</sup> Importantly, whereas most global FDI waves have been associated with an increase in mergers and acquisitions (M&A) (Brakman et al., 2006), the extent to which greenfield (GF) investments contribute to surges and stops in FDI in developing countries remains unclear. It is, however, important to explore this question because GF investments dominate FDI flows to the developing world (Markusen and Stähler, 2011; UNCTAD, 2012), especially in resource-rich countries where local companies often have privileged access to the resources and, hence, host country government policies encourage GF investments into joint ventures. It is also true in low-income countries where large price differentials between the home and host countries and the absence of attractive

> 3 However, both modes are associated with increases in aggregate productivity.

> 4 M&A sales create rents for the previous owners which are not necessarily channeled into new investments (Harms and Méon, 2012). Yet, M&As might rely more on local and regional supplier networks

> than multinationals entering through greenfield investments (Wes and Lankes, 2001).

3
corporate assets make GF FDI more likely as an entry mode. Our focus on the developing world is also motivated by the study of Blonigen and Wang (2005) who show that the determinants of FDI flows to developing countries differ from those to developed ones.

Understanding the role played by the mode of entry in the incidence of FDI surges and stops is valuable in the context of rising FDI flows to the developing world. These types of flows have become an important and sometimes dominant source of finance in developing countries, so there is a concern that economic growth might be harmed in countries exposed to extreme fluctuations of either type of these flows (Lensink and Morrissey, 2006; Herzer, 2012). There is also the long-standing concern that sudden stops and surges in foreign capital flows might contribute to and arise as a result of macroeconomic volatility (Calvo et al., 2006) and crises (Reinhart and Reinhart, 2009; Furceri et al., 2012) as well as complicate macroeconomic management in developing economies. Abiad et al. (2011) and Cowan and Raddatz (2011), for instance, point to a connection between sudden stops and credit market imperfections. Gall et al. (2013) find that high past exposure to FDI flows may impede an economy’s ability to respond to sudden stops in FDI, especially in industries relying on external financing, and more so in countries with less developed financial markets.

The paper is related to the broader literature on net capital flows, which are volatile, procyclical, and, during crises, prone to large “sudden stops,” defined as sharp slowdowns in net capital inflows. The literature originated with Calvo (1998) and broadened to include different conditions as well as the opposite events such as “surges”, defined as sharp 4
increases in net capital flows (Reinhart and Reinhart, 2009).<sup>5</sup> However, this paper studies the behavior of gross FDI flows to developing countries as we are interested in surges and stops due to actions of foreigners. Cowan et al. (2008) and Rothenberg and Warnock (2011) make the point that measures of “sudden stops” constructed from data on net inflows are not able to differentiate between stops that are due to the actions of foreigners and those due to locals fleeing the domestic markets. In addition, Broner et al. (2013) show that gross capital flows are pro-cyclical and are larger and more volatile than net capital flows.

While Levchenko and Mauro (2007) show that FDI is the least volatile form of financial flow, when the average size of net or gross flows is taken into account, this paper shows that FDI surges and stops in the developing world are not rare events<sup>6</sup> and therefore are worth an in-depth look. Specifically, the paper contributes to the literature in the following ways. First, we build a database of episodes when foreign investors substantially increase or decrease FDI inflows to a developing country and distinguish between these episodes based on the dominance of the mode of entry. Using this database, which covers the period from 1990 to 2010 and includes 95 developing economies, we then document the incidence of sudden stops and surges by mode of entry, region, and resource status of the receiving economy. Second, we identify the factors associated with FDI surges and stops by mode of entry (i.e. GF-led and M&A-led surges and stops). We show that GF-led and M&A-led extreme events such as surges and stops have different determinants and therefore must be studied separately.

> 5 Other papers that belong to this literature include, for example, Kaminsky et al. (1998), Levchenko and

> Mauro (2007), and Mendoza (2010).

> 6 All developing countries experienced at least one such event during the period of investigation and most countries experienced multiple FDI surges and stops.

5
Our approach yields different results from previous studies on surges and stops in FDI flows which do not differentiate between these events based on the mode of entry (e.g., Dell’Erba and Reinhart, 2012). We show that different factors are associated with the onset of GF-led and M&A-led FDI surges and stops. Global liquidity is the only common predictor of the two types of FDI surges, while a decline in global growth and a FDI surge in the preceding year are the only significant and consistent predictors of FDI stops. GF-led sudden stops and surges are more likely in lower income and resource-rich countries than elsewhere. Policies aimed at increasing financial openness are enablers of M&A-led surges, which are also more likely during periods of global growth and domestic economic and financial instability. The results are also policy relevant as we show that GF-led extreme events occur more frequently than M&A-led ones.<sup>7</sup> Thus, countries relying mostly on GF investments, the more stable type of FDI inflows, are not immune to sudden stops in capital flows and should prepare to withstand them. Knowledge of the factors behind different types of FDI surges and stops can help policy makers in developing countries craft policies to successfully weather such episodes.

The remainder of this paper is organized as follows. The next section defines FDI surges and stops and presents information on the types and frequency of such surges. In section 3, we turn attention to the empirical models for predicting GF-led and M&A-led surges and stops, the discussion of the econometric results, and the robustness checks. Section 4 summarizes the findings and offers concluding remarks.

# **2. Identifying GF-led and M&A-led FDI Surges and Stops**

Using UNCTAD data on gross FDI inflows from the World Investment Report (UNCTAD, 2011) and building on the work by Calvo et al. (2004), Reinhart and Reinhart

> 7 However, M&A-led surges are more likely to be short-lived and followed by a stop.

6
(2009), and Forbes and Warnock (2012), we define a surge episode as an increase in inflows in a given year that is more than one standard deviation above the countryspecific (five-year rolling) average. The surge episode begins when the FDI-to-GDP ratio increases more than one standard deviation above its rolling mean and ends when the FDI-to-GDP ratio falls below one standard deviation above its rolling mean. In addition, we pose a restriction to the definition of an FDI surge in that the increase in the FDI-toGDP ratio should fall within the top 25<sup>th</sup> percentile of the entire sample’s FDI-to-GDP ratio growth. This not only ensures that the increase in FDI inflows is substantial, but also that only large surges by international standards are included in our definition of a surge (Ghosh et al., 2012).

This approach combines the two main empirical strategies present in the literature on surges and stops. One involves looking at deviations from the mean while the other requires factoring in minimum threshold values. Stops are defined in a symmetric way, with a stop episode defined as a decline in inflows in a given year that is more than one standard deviation below the rolling average. The stop episode starts when the ratio of FDI to GDP declines more than one standard deviation below its rolling mean and ends when the ratio increases above one standard deviation below its mean. We impose similar restrictions on stops as on surges.

To identify whether a FDI surge or a stop can be _mainly_ attributed to an increase in M&A activity or GF investments, we use Thompson ONE Source data on M&A inflows, available from 1990 onwards. Following Calderon et al. (2004), Wang and Wong (2009), and Bogach and Noy (2013), GF FDI is defined as the difference between gross FDI and M&A inflows. Using this information, we assess whether a surge in a given year is dominated by an increase in M&A activity or by an increase in GF investments. A surge is M&A-led when more than half of the increase in FDI can be attributed to an increase 7
in M&A activity. Likewise, a surge is GF-led when more than half of the increase in FDI can be attributed to an increase in GF investments.

As indicated by Calderon et al. (2004), there are several potential problems with combining aggregate FDI inflow data with M&A data. First, FDI flows are measured on an accruals basis, while M&As are recorded at the time of an announcement or closure of a deal. As such, an FDI surge in a given year, for example, may be attributed to a sharp increase in M&A in the year before. Second, calculating GF FDI as the residual of FDI and M&A may possibly pollute the data with some international transactions that are not GF FDI. We control for these problems by verifying whether a FDI surge can indeed be attributed to either an increase in M&A or GF FDI and correct for anomalies in the data by accounting for the share of M&A activity in the year before the surge. When values for M&A activity are missing, it is assumed that the surge could be attributed to a surge in GF FDI. Exactly the same procedure is applied to identifying GF-led and M&A-led FDI stops.

Figure 1 on FDI surges and stops in Algeria illustrates how such events are identified. The left panel of Figure 1 assesses the first condition, namely that we speak of a surge if the FDI-to-GDP ratio increases more than one standard deviation above its five year rolling mean. We see that in Algeria this is the case in several years in the mid1990s and the early and late 2000s. Likewise, a stop is identified when the FDI-to-GDP ratio decreases more than one standard deviation below its five year rolling mean. This is the case in 1992, 2003 and 2010. However, in order to qualify for a surge, the increase should fall within the top 25<sup>th</sup> percentile of the entire sample’s FDI-to-GDP ratio growth, meaning an increase of at least 0.82 percentage points, marked by the top horizontal line in the right panel of Figure 1. Likewise, in order to qualify for a stop, the increase should fall within the bottom 25<sup>th</sup> percentile of the entire sample’s FDI-to-GDP ratio growth, 8
<!-- Start of picture text --> 7<br>oSac $iY<br>o<br>5<br>&<br>a<br>9Q<br>ao sere, - a<br>aus<br>S 1g<br>= \/<br>OL'<br>1990 1995 2000 2005 2010<br>Year<br>——— Change inFDIIGDP = ~*~ =» Change in M&AVGDP<br>vrvrrereeee Change in GF/GDP<br><!-- End of picture text --> <!-- Start of picture text --> a”<br>a3c<br>5 F \<br>& con i ‘<br>a < Newesaglher”<br>fa9g ’ oT<br>ac = ea ““<br>u ee =<br>s sary f<br>8 Ne ~"- _-_<br>A pee?<br>oT' wae¥<br>1990 1995 2000 2005 2010<br>Year<br>ss--*ee-* 1 SD above Rolling Mean —~~-~—-~- 1 SD below Rolling Mean<br>——— Change in FDI/GDP<br><!-- End of picture text -->
Although M&A flows are much more volatile than GF flows,<sup>9</sup> GF-led FDI surges outnumber M&A-led FDI surges in developing countries: around 80% of the FDI surges in developing economies can be attributed to an increase in GF FDI. Regions with either a high share of resource-rich or low-income countries or both such as the Middle East and North Africa and Sub-Saharan Africa, where GF investments represent a large share of FDI flows, have had the highest occurrence of GF-led FDI surges (Table 1). In resource-rich countries, governments encourage GF investments as local firms typically have privileged access to the resources. In low-income economies, large labor cost differentials between the home and host economies make GF FDI more likely as an entry mode. Regions with relatively strong links to global financial markets have had the lowest incidence of GF-led FDI surges and the highest incidence of M&A-led FDI surges. In addition, our analysis suggests that some countries experience more FDI surges than others and FDI surges occur at different times in different developing countries and usually last only a year. They were most prevalent in Europe and Central Asia and the Middle East and North Africa in the mid-2000s, in East Asia and Pacific and Sub-Saharan Africa in the late 1990s and late 2000s, and in Latin America and Caribbean in the mid-1990s.

We identify FDI stops in a symmetric way. The 95 developing economies in the sample experienced 282 stop-years during the period 1991-2010, of which 225 were GF-led stops and 57 were M&A-led stops(see also Appendix A1). The unconditional probability of experiencing a GF-led stop was 12.8%, while that for an M&A-led stop was 3.3%. All countries in the sample experienced at least one stop-year and most stops were GF-led. Yet, M&A-led surges are significantly more frequently followed by a stop in the next year (51%) than GF-led surges (28%) (p-value for the Fischer’s exact test < 0.01). This

> 9 Whereas the average coefficient of variation for M&A flows was 4.00, the coefficient of variation for GF flows was only 0.82. The average coefficient of variation is based on the mean value for the coefficients of variation for all countries in the sample.

10
suggests that M&A-led surges are more likely to be short-lived and followed by a stop than GF-led events.

**Table 1: Incidence and Types of FDI Surges and Stops in Developing Countries**

||Incidence of<br>Surge|% GF-led<br>Surge|Incidence of<br>Stop|% GF-led<br>Stop|
|---|---|---|---|---|
|East Asia and Pacific|15.5%|72.3%|15.0%|67.6%|
|Europe and Central Asia|16.5%|54.5%|21.9%|65.6%|
|Latin America and Caribbean|15.8%|68.2%|14.5%|67.2%|
|Middle East and North Africa|13.1%|83.3%|15.7%|85.7%|
|South Asia|6.3%|80.0%|8.8%|77.8%|
|Sub-Saharan Africa|15.5%|89.2%|14.1%|92.6%|
|Resource-Rich Economies<sup>*</sup>|15.3%|87.9%|15.4%|86.7%|
|All Economies in Sample|14.9%|78.4%|15.1%|80.3%|

* Hydrocarbon and Mineral Rich Countries as defined by IMF

Although at the global level the unconditional probability of experiencing a surge is similar to that of a stop, the occurrence of surges varies by region and over time. Stops are more frequent in Europe and Central Asia than in the other world regions and least frequent in lower income developing countries, but differences between different country groups are never statistically significant.<sup>10</sup> As in the case of FDI surges, stops occur at different times in different developing countries and most last only a year.

# **3. Predicting GF-led and M&A-led FDI Surges and Stops**

# Estimation Approach and Variables

We inform the selection of variables that might be associated with a FDI surge or a stop by drawing on the literature on sudden stops and bonanzas. Following this literature, we conjecture that the probability of a GF-led and M&A-led surge or stop depends on three sets of factors – global, contagion (regional), and domestic (Calvo et al., 1996; Fernandez-Arias and Montiel, 1996; Dell’Erba and Reinhardt, 2012; and Forbes and

> 10 This inference is based on a Fischer’s exact test.

11
Warnock, 2012). Hence, to examine the role of these global, contagion, and domestic factors in the conditional probability of having a GF-led or M&A-led FDI surge or a stop, we estimate the model: The variable ε _it_ is an indicator of the occurrence of an event in country _i_ and year _t_ and is equal to one of six episode dummy variables defined as follows. The first one, _sit,_ assumes the value 1 if there is a FDI surge, either GF-led or M&A-led one, in country _i_ and year _t._ In all other cases, _sit_ is 0. The dummy variable, _hit,_ equals 1 if a country _i_ is experiencing a GF-led surge in a given year _t_ and 0 otherwise. Finally, the dummy variable _mit_ is 1 in the case of an M&A-led surge in a given year _t_ and country _i_ , and 0 otherwise. In a similar fashion, three dummy variables for stops are defined: one for GF-led FDI stops, one for M&A-led FDI stops, and one for FDI stops, regardless of their kind (GF-led or M&Aled). _G_ is a vector of variables capturing global factors, _R_ is a vector of variables capturing regional factors, and _D_ is a vector of variables capturing domestic factors.

Since surges and stops occur irregularly, (.) is asymmetric and, therefore, we use the complementary logistic regression (see also Forbes and Warnock, 2012) which assumes that (.) is the cumulative distribution function (cdf) of the extreme value distribution. We estimate the model separately for the six types of events; for the regressions on the likelihood of GF-led and M&A-led surges and stops, covariance across surge-years and stop-years is accounted for using seemingly unrelated regression, estimated with clustering of standard errors at the country level. The variables representing domestic stop or surge, GDP per capita, and natural resources are lagged by one year, and the latter two are winsorized at the 1% level.

12
Since economic developments in developed markets, which are the primary source of this type of finance, trigger big fluctuations in FDI flows to developing countries (Aleksynska and Havrylchyk, 2013), we include several global factors, including global risk, global liquidity, and global growth. Global risk is a volatility measure given by the VXO index of the Chicago Board Options Exchange. Global liquidity measures the availability of finance in global markets and is given by the sum of the change in the following two ratios – the ratio of stock market capitalization to GDP and the ratio of domestic private sector credit to GDP (Beck et al., 2000).<sup>11</sup> The size of the financial market is expected to be positively related to the ability to mobilize capital. Global growth measures the real growth of the world economy and is obtained from the World Development Indicators. Regional contagion reflects the extent to which surges or stops occurred in the region of the country experiencing the surge. The indicator used to measure this factor is the share of countries in the same macro region which experienced a surge in the preceding year (see also Dell’Erba and Reinhardt, 2012).

The domestic set of factors include experiencing a surge in the preceding year, experiencing a stop in the preceding year (see also Sula, 2010),<sup>12</sup> per capita GDP, natural resource rents as a share of GDP, and the change in the following set of variables – trade and financial openness, economic and financial stability, and political stability. We expect M&A-led surges and stops to be more prevalent in higher income developing economies (Nocke and Yeaple, 2008; Qiu and Wang, 2011) because of the presence of attractive corporate assets in terms of quality of inputs and technology<sup>13</sup> and a narrower gap in

> 11 See Forbes and Warnock (2012) for a similar operationalization of this variable.

> 12 It can be expected that some surges concur with the recovery from a stop in FDI and some stops happen after a sudden surge in FDI.

> 13 Foreign firms typically ‘cherry pick’ high quality targets (Bertrand et al., 2012).

13
production costs between the destination and the source country.<sup>14</sup> A large price differential between the home and host countries might make GF investments more likely as an entry mode in lower income developing economies. These differentials are needed to offset the relatively higher start-up costs associated with the construction of new facilities. Investments in resource-intensive industries also usually take the form of GF FDI. The reason for this is that local companies often have privileged access to these natural resources and, hence, host country governments prefer joint ventures in the form of GF FDI (Demirbag et al., 2008). M&A-led surges and stops are also more likely in riskier and uncertain macroeconomic environments because of the existence of discounts on the prices of existing assets (Buiter et al., 1998). Such events have been associated with fire-sale FDI during the Latin American and Asian financial crises of the 1990s (Krugman, 2000; Aguiar and Gopinath, 2005). M&A-led surges and stops have been encouraged by capital market imperfections that result in undervaluations of firm assets, sales of assets at unrealistic prices, and stripping of firms for purely financial gains. Changes in financial openness in particular might have an effect on the likelihood of an M&A-led surge or stop. Furthermore, the existence of capital controls in the form of restrictions on foreign ownership and short-selling may limit possibilities for the earliermentioned fire-sale FDI.<sup>15</sup> There seems to be a strong political preference for GF investments, which are perceived to be more beneficial than M&As (Heinemann, 2012). Domestic unrest can also deter some types of FDI or trigger the pullout of investors from some markets (Schneider and Frey, 1985). Still, results from econometric studies

> 14 Aleksynska and Havrylchyk (2013) estimated that for the period 1996-2007, 56% of all FDI into developing countries originated from developed countries.

> 15 Likewise, government interventions against the takeover of domestic companies through cross-border

> M&A have become more common recently (Heinemann, 2012).

14
are ambiguous about the relationship between the degree of political stability in a country and stops in FDI inflows (Salomon and Ruiz, 2012).

The source for data on GDP, trade, and natural resource rents is the World Bank’s World Development Indicators. Financial openness is represented by the Chinn and Ito (2008) capital account openness index. The International Country Risk Guide is the source for the economic, financial, and political stability measures. Definitions and sources for the independent variables are presented in Appendix A2, while descriptive statistics can be found in Appendix A3.

# Econometric Results

The results from the complementary logistic regressions presented in Table 2 suggest that in general FDI surges are difficult to predict. The baseline regressions have low pseudo R<sup>2</sup> varying between 3% and 10% of the observed variance.<sup>16</sup> Stops appear easier to predict in that the pseudo R<sup>2</sup> varies between 10% and 34%. This difference can be attributed mainly to the fact that a surge in the preceding year is a good predictor of a FDI stop, but not vice versa. Hence, it can be inferred that surges are followed by stops. At the same time, a FDI surge in the preceding year is not a good predictor of a surge and a FDI stop in the preceding year is not a good predictor of a stop.

# _<u>FDI Surges</u>_

Global liquidity is associated positively with a FDI surge, regardless of its kind. Although global liquidity is only significantly correlated with the probability of a GF-led surge (Table 2), the effect of global liquidity on the likelihood of a GF-led surge does not significantly differ from the effect of global liquidity on the likelihood of an M&A-led surge (χ<sup>2</sup> =0.08, p=0.77). Furthermore, the effect of global liquidity on the likelihood of

> 16 This figure is based on McFadden’s R2.

15
an M&A-led surge becomes significant under alternative definitions of a surge (Appendix Table B3),<sup>17</sup> alternative definitions of a resource-rich economy, economic, financial and political stability (Appendix Table B7), and additional control variables (Appendix Table B11). Overall, these results suggest that loosening of global credit conditions in response to global financial crises tends to increase the frequency of FDI surges in developing countries. These results are in line with earlier findings of Di Giovanni (2005) and Baker _et al._ (2009) that the availability of cheap financial capital stimulates the expansion of multinational activity.

Regional contagion increases the probability of a surge, but not of a stop (Table 2). The result is robust when we use random effects probit, which allows for country-specific unobserved heterogeneity (Table 3). The conclusions that can be drawn from this estimation are to a large extent identical to the ones from a seemingly unrelated estimated complementary logistic regression with cluster-robust standard errors presented in Table 2. Likewise, considering a more restricted sample of events (Table 4), which excludes surges followed by stops and focuses on the so-called “sustainable” surges, did not lead to substantially different results. However, when we only focus on “sustainable” M&Aled surges and “relentless” stops (stops followed by a stop and not a surge), the effect of regional contagion on the probability of a surge becomes insignificant.

There are pronounced differences with regard to the factors that predict the onset of M&A-led and GF-led surges. Global growth is positively and significantly correlated with the incidence of M&A-led surges and FDI-surges in general, but not with GF-led surges in general. This difference in effect sizes between M&A-led and GF-led surges was

> 17 The increase in the FDI-to-GDP ratio is more than one and a half standard deviation above its rolling

> mean.

16
statistically significant (χ<sup>2</sup> =16.56, p<0.01). The result is robust across different estimation strategies, alternative definitions of surges, other variables’ definitions, and additional control variables (see results in Table 3, Appendix Tables B1 and B3, Appendix Table B5 and B7, and Appendix Table B9 and B11). In the case of M&A-led surges, the result is in line with the fact that the two most recent global FDI and M&A waves took place during periods of strong economic growth, and their ends coincided with global downturns. In the case of GF-led surges, it reflects the fact that firms are often driven to invest in operations located in developing countries as a cost cutting measure and not necessarily during periods of strong global growth. However, when we only focus on sustainable surges, we find that global growth is a significant predictor for sustainable GF-led surges.

A country with lower per capita income level is significantly more likely to experience GF-led surges (Table 2). In contrast, M&A-led surges are significantly more likely in countries with higher per capita incomes. The difference in the effect of per capita income levels on the likelihood of having a GF-led versus M&A-led surge is statistically significant (χ<sup>2</sup> =13.10, p<0.01). These results are robust to alternative estimation (Table 3), specification methods (Appendix Tables 9 and 11), and different definitions of a surge (Appendix Tables 5 and 7).

Resource-rich countries are more likely to experience a GF-led surge, but less likely to incur an M&A-led surge. This result is not significant across estimation techniques and variable definitions, and when additional control variables are added. However, the difference in the effect sizes is statistically significant (χ<sup>2</sup> =5.01, p=0.025) and this result holds when we replace the natural resource variable with a dummy variable that takes the value 1 if a country is hydrocarbon or mineral-rich as defined by the IMF (Appendix B5 and B7; χ<sup>2</sup> =7.26, p<0.01).

17
In line with the fire-sale FDI hypothesis of Krugman (2000) and Aguiar and Gopinath (2002) and as shown by Bogach and Noy (2013), M&A-led surges are significantly more likely in countries which experience deterioration in economic and financial stability. Although an improvement in economic and financial stability tends to be positively associated with the probability of a GF-led surge, the variable is not significant in the baseline model and alternative tests. These results are robust to changes in the model specification, estimation technique, and variables definitions. The difference between the effects of changes in economic and financial stability on the likelihood of the two types of FDI surges is statistically significant (χ<sup>2</sup> =7.83, p<0.01) and becomes more pronounced under stricter definitions of a surge. The findings are supported by the results when additional control variables related to changes in domestic macroeconomic economic conditions are added to the model (Appendix Tables B9 and B11). A decrease in the exchange rate and an increase in the inflation rate are positively associated with the probability of M&A-led surges, but not of GF-led surges. This difference in effect size of the exchange rate variable is statistically significant (χ<sup>2</sup> =9.35, p<0.01).

A decrease in capital controls (i.e. increase in financial openness) is associated with a higher probability of an M&A-led surge, but not with a higher probability of a GF led surge. This finding is in line with the idea that there is a strong political preference for GF FDI and capital controls particularly affect FDI in the form of M&As. The result is robust across different definitions of a surge and independent variables, as well as additional controls, but not alternative estimation techniques. Furthermore, the difference between the effects of changes in financial openness on the probability of the two types of FDI surges is not statistically significant. Finally, global risk and changes in political stability do not affect the likelihood of an FDI surge regardless of its type in nearly all specifications.

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# _<u>FDI Stops</u>_

An FDI surge in the preceding year is the only significant and robust predictor of an FDI stop, regardless of its kind (Table 2). This result is in line with the recent findings of Agosin and Huaita (2012) who show that the best predictor of a sudden stop is a preceding capital boom, where stops are downward overreactions to sharp preceding positive overreactions. Tightening of global credit conditions diminishes the frequency of FDI surges, but conditional on a surge in the previous year do not necessarily have a contemporaneous effect on the likelihood of FDI stops. However, since global liquidity is a significant predictor of FDI surges, and a surge is a significant predictor of a stop in the next period, global liquidity is an important predictor of both FDI surges and stops.

A decline in global growth has a significant and positive effect on the likelihood of a FDI stop, regardless of its kind, where the difference in effect sizes between the two modes of entry is not statistically significant (χ<sup>2</sup> =0.79, p=0.38). This result, however, loses its significance under stricter definitions of stops, be they GF-led or M&A-led (Appendix Tables B2 and B4).

GF-led and M&A-led FDI stops are, on average, more likely in poorer and richer countries, respectively. The effect itself is not significant, but the difference in effect sizes is statistically significant (χ<sup>2</sup> =4.57, p=0.033) and becomes pronounced under stricter definitions of FDI stops (Appendix Tables B2 and B4). This difference in effect sizes holds under alternative estimation methods and variable definitions and when additional variables are included.

The frequency of FDI stops is not higher in resource-rich countries than elsewhere in the world. Resource-rich economies appear to be more likely to have GF-led episodes and less likely to have M&A-led episodes, but these average effects are not significant across 19
a range of alternative model and variables’ specifications. Moreover, the difference in effect sizes of natural resources on GF-led and M&A-led stops is not statistically significant (χ<sup>2</sup> =1.70, p=0.192) in the baseline model in Table 2 as well as in the model using the alternative definition of natural resources (Appendix Tables B6 and B8; χ<sup>2</sup> =1.29, p=0.257).

Removal of capital controls increases the probability of an M&A-led stop, but not of a GF-led stop. The difference in effect sizes is statistically significant (χ<sup>2</sup> =7.05, p<0.01) and holds when we control for changes in trade openness and tariffs (Appendix Tables B10 and B12), but this result becomes less pronounced under stricter definitions of a stop (Appendix Tables B2 and B4). Global risk and changes in political, economic, and financial stability do not affect the likelihood of a FDI stop regardless of its type and model specification.

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**Table 2: Regression Results on Likelihood of a FDI Surge and Stop – Complementary Logistic Regression Estimates**

|||**Surges**<br>|||**Stops **<br>||
|---|---|---|---|---|---|---|
||**All**|**GF-led**|**M&A-led**|**All**|**GF-led**|**M&A-led**|
|**Global Factors**|||||||
|Global Risk|0.015(.013)|0.010(.014)|0.045(.028)|0.023(.010)*|0.013(.013)|0.046(.031)|
|Global Liquidity|0.014(.003)**|0.013(.004)**|0.015(.007)|0.003(.003)|0.002(.003)|0.003(.006)|
|Global Growth|0.150(.055)**|0.076(.058)|0.542(.097)**|-0.127(.042)**|-0.112(.053)*|-0.235(.112)*|
|**Regional Contagion**|0.020(.006)**|0.021(.008)*|0.032(.015)*|0.000(.006)|-0.007(.010)|0.028(.017)|
|**Domestic Factors**|||||||
|Stop previousyear|0.217(.180)|0.263(.231)|0.685(.501)|-0.391(.218)|-0.735(.332)*|0.848(.612)|
|Surgepreviousyear|-0.182(.183)|-0.014(.205)|-0.195(.540)|1.623(.141)**|1.533(.153)**|3.915(317)**|
|GDPper Capita(ln)|-0.082(.023)**|-0.148(.034)**|0.173(.068)*|-0.008(.028)|-0.067(.036)|0.106(.068)|
|Natural Resources|0.001(.003)|0.007(.003)*|-0.036(.018)*|0.002(.003)|0.006(.003)*|-0.007(.009)|
|Δ Financial Openness|0.181(.132)|0.118(.165)|0.433(.212)*|-0.069(.156)|-0.247(.195)|0.708(.247)**|
|Δ Economic and Financial<br>Stability|0.002 (.013)|0.020 (.015)|-0.068 (.027)*|-0.004 (.017)|-0.011 (.018)|0.026 (.031)|
|Δ Political Stability|-0.030(.018)|-0.038(.020)|0.011(.042)|-0.025(.019)|-0.031(.021)|0.013(.041)|
|Number of Observations|1768|1768|1768|1768|1768|1768|
|Number of Countries|95|95|95|95|95|95|
|Number of Surges or Stops|264|207|57|282|225|57|
|Pseudo R<sup>2</sup>|0.034|0.033|0.102|0.125|0.104|0.340|
|*p<0.05,**p<0.01;cluster-robust|standard errors inpare|ntheses.|||||

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**Table 3: Regression Results on Likelihood of a FDI Surge and Stop – Random Effects Probit Estimates**

|||**Surges**|||**Stops **||
|---|---|---|---|---|---|---|
||**All**|**GF-led**|**M&A-led**|**All**|**GF-led**|**M&A-led**|
|**Global Factors**|||||||
|Global Risk|0.010(.007)|0.006(.007)|0.024(.012)|0.013(.008)|0.007(.008)|0.021(.016)|
|Global Liquidity|0.008(.002)**|0.007(.002)**|0.006(.004)|0.001(.002)|0.001(.002)|0.001(.003)|
|Global Growth|0.086(.032)**|0.038(.033)|0.250(.068)**|-0.094(.030)**|-0.076(.031)*|-0.110(.056)|
|**Regional Contagion**|0.012(.004)**|0.013(.005)*|0.019(.010)*|0.033(.372)|-0.372(.472)|0.014(.011)|
|**Domestic Factors**|||||||
|Stop previousyear|0.140(.103)|0.172(.117)|0.327(.265)|-0.221(.132)|-0.368(.163)*|0.342(.326)|
|Surgepreviousyear|-0.101(.110)|-0.004(.125)|-0.132(.329)|1.108(.131)**|1.003(.102)**|2.261(.199)**|
|GDPper Capita(ln)|-0.048(.021)*|-0.083(.023)**|0.082(.036)*|-0.002(.022)|-0.036(.023)|0.077(.042)|
|Natural Resources|0.001(.002)|0.004(.002)|-0.013(.005)*|0.002(.003)|0.004(.002)|-0.002(.005)|
|Δ Financial Openness|0.181(.132)|0.078(.096)|0.166(.155)|-0.039(.096)|-0.131(.102)|0.316(.161)*|
|Δ Economic and Financial<br>Stability|0.002 (.013)|0.013 (.009)|-0.030 (.015)*|-0.004 (.009)|-0.007 (.009)|0.016 (.016)|
|Δ Political Stability|-0.018(.012)|-0.022(.013)|0.009(.021)|-0.018(.013)|-0.018(.013)|-0.002(.025)|
|Number of Observations|1768|1768|1768|1768|1768|1768|
|Number of Countries|95|95|95|95|95|95|
|Number of Surges or Stops|264|207|57|282|225|57|
|*p<0.05, **p<0.01; cluster-robust<br>M&A dominated surge). The varia<br>windsorized at the 1% level.|standard errors in pare<br>bles Regional Contagio|ntheses. Dependent var<br>n, GDP per capita and|iable is a 0-1 variable ind<br>Natural Resources are la|icating if there is a surg<br>gged (one year). The var|e or stop episode (eithe<br>iables GDP per capita|r GF-led surge or an<br>and natural resources are|

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# **Table 4: Sustainable Surges and Relentless Stops**

||**Su**<br>|**Sustainable Surg**<br>**rges not followed b**<br>|**es:**<br>**y Stops **<br>|**St**<br>|**Relentless Stop**<br>**ops not followed by **<br>|**s:**<br>**Surges**<br>|
|---|---|---|---|---|---|---|
||**All**|**GF-led**|**M&A-led**|**All**|**GF-led**|**M&A-led**|
|**Global Factors**|||||||
|Global Risk|-0.006(.007)|0.002(.017)|0.025(.043)|0.024(.013)|0.015(.014)|0.051(.034)|
|Global Liquidity|0.015(.005)**|0.011(.006)*|0.017(.011)|0.003(.003)|0.002(.003)|0.001(.007)|
|Global Growth|0.215(.088)*|0.195(.092)*|0.710(.180)**|-0.143(.051)**|-0.117(.059)*|-0.237(.118)*|
|**Regional Contagion**|0.023(.007)**|0.020(.009)*|0.027(.024)|0.002(.675)|-0.010(.011)|0.025(.018)|
|**Domestic Factors**|||||||
|Stop previousyear|0.351(.240)|0.444(.281)|0.963(.653)|-0.352(.268)|-0.636(.358)|1.062(.619)|
|Surgepreviousyear|-0.422(.280)|-0.293(.320)|0.176(.596)|1.663(.142)**|1.559(.154)**|4.098(.34)**|
|GDPper Capita(ln)|-0.051(.044)|-0.109(.052)*|0.226(.103)*|-0.027(.038)|-0.091(.038)*|0.084(.079)|
|Natural Resources|0.003(.005)|-0.000(.005)|-0.021(.018)|0.002(.004)|0.006(.003)*|-0.004(.010)|
|Δ Financial Openness|0.166(.190)|0.049(.211)|0.768(.261)**|0.141(.166)|-0.027(.183)|0.801(.241)**|
|Δ Economic and Financial<br>Stability|0.013 (.018)|0.029 (.017)|-0.087 (.037)*|0.002 (.015)|-0.002 (.019)|0.029 (.033)|
|Δ Political Stability|0.002(.023)|-0.002(.023)|-0.031(.057)|-0.033(.023)|-0.038(.023)|0.019(.043)|
|Number of Observations|1768|1768|1768|1768|1768|1768|
|Number of Countries|95|95|95|95|95|95|
|Number of Surges or Stops|152|132|29|228|188|51|
|*p<0.05, **p<0.01; cluster-robust<br>M&A-led surge). The variables Re<br>windsorized at the 1% level.|standard errors in pare<br>gional Contagion, GDP|ntheses. Dependent var<br>per capita and Natural|iable is a 0-1 variable ind<br>Resources are lagged (o|icating if there is a surg<br>ne year). The variables|e or stop episode (eithe<br>GDP per capita and nat|r GF-led surge or an<br>ural resources are|

23
# Sensitivity Analysis

This section provides an overview of the most important results from the sensitivity analysis presented in Appendix B, but not discussed in the previous paragraph. The estimations which use alternative variable definitions (Appendix Tables B5-B8) and additional control variables (Appendix Tables B9-B12) did not yield very different results.

Differences with the baseline regression under different definitions of FDI surges and stops (Appendix Tables B1-B4) were most pronounced. Under the strictest definition the number of surges and stops falls down to 143 (8.0% of the all country-years) and 108 (6.1% of all the country-years), respectively. Compared to the baseline regressions, the effect of global growth on the likelihood of GF-led and M&A-led stops, the effect of regional contagion on the likelihood of GF-led surges, and the effect of a stop in the preceding year on GF-led stops lose their significance under stricter definitions of a surge or stop. In addition to the differences described above, a decrease in political stability has a positive and significant effect on the likelihood of a GF-led surge under some of the stricter definitions of a surge. This result can be explained by the fact that some firms try to take advantage of new opportunities that arise through the changes in political regimes and reap the benefits in conflict locations through first mover advantages or market power. In particular, firms in the primary sector (which mainly enter through GF FDI) would be less deterred by a decrease in political stability because activities in this sector are bound by physical geography and risk adjusted rents are higher (Burger et al. 2013).

# **4. Concluding Remarks**

This paper investigates the factors triggering FDI surges and stops episodes, differentiated based on whether these events are led by waves in GF investments or M&As. In particular, we analyze the effect of the mode of entry on the incidence and 24
determinants of FDI surges and stops in the developing world. The focus on this topic is warranted because during the past decade there has been a significant increase of FDI flows to developing countries but the rise has not proceeded in a smooth fashion, prompting concerns about sudden stops even in countries where FDI inflows dominate capital flows. Furthermore, whereas most global FDI waves have been associated with an increase in M&As, it is not clear to what extent GF investments have contributed to surges and stops in FDI in developing countries. It is important to answer this question because GF investments dominate FDI flows to the developing world, especially in resource-rich and low-income countries, and as shown in the paper, GF-led surge and stop episodes occur more frequently than M&A-led ones.

This paper contributes to the literature by constructing a database of episodes when foreign investors substantially increase or decrease FDI inflows to developing countries and distinguish between these episodes based on the dominance of the entry mode. We use this database to document the incidence of FDI surges and stops by mode of entry, region, and resource status. Using this database, we analyze the factors associated with GF-led and M&A-led surge and stop events and show that the two types of surges and stops have different incidence and determinants, and therefore must be studied separately. Our analysis shows that global liquidity is the only common predictor of different types of FDI surges, while a decline in global growth and an FDI surge in the preceding year are the only significant and consistent predictors of FDI stops. GF-led stops and surges are more likely in low income and resource-rich countries than elsewhere. Policies aimed at increasing financial openness are enablers of M&A-led surges, which are also more likely during periods of global growth and domestic economic and financial instability.

25
The results are policy relevant as the paper shows that GF-led extreme events occur more frequently than M&A-led ones. Thus, countries relying mostly on GF FDI, the more stable type of FDI inflows, are not immune to sudden stops in capital flows and should prepare to withstand them. Knowledge of the factors behind different types of FDI surges and stops can help policy makers in developing countries, especially those relying heavily on FDI flows, prepare to weather such events.

26