Surges
  • 1 0000000404811396https://isni.org/isni/0000000404811396International Monetary Fund

Contributor Notes

This paper examines why surges in capital flows to emerging market economies (EMEs) occur, and what determines the allocation of capital across countries during such surge episodes. We use two different methodologies to identify surges in EMEs over 1980-2009, differentiating between those mainly caused by changes in the country's external liabilities (reflecting the investment decisions of foreigners), and those caused by changes in its assets (reflecting the decisions of residents). Global factors-including US interest rates and risk aversion¡-are key to determining whether a surge will occur, but domestic factors such as the country's external financing needs (as implied by an intertemporal optimizing model of the current account) and structural characteristics also matter, which explains why not all EMEs experience surges. Conditional on a surge occurring, moreover, the magnitude of the capital inflow depends largely on domestic factors including the country's external financing needs, and the exchange rate regime. Finally, while similar factors explain asset- and liability-driven surges, the latter are more sensitive to global factors and contagion.

Abstract

This paper examines why surges in capital flows to emerging market economies (EMEs) occur, and what determines the allocation of capital across countries during such surge episodes. We use two different methodologies to identify surges in EMEs over 1980-2009, differentiating between those mainly caused by changes in the country's external liabilities (reflecting the investment decisions of foreigners), and those caused by changes in its assets (reflecting the decisions of residents). Global factors-including US interest rates and risk aversion¡-are key to determining whether a surge will occur, but domestic factors such as the country's external financing needs (as implied by an intertemporal optimizing model of the current account) and structural characteristics also matter, which explains why not all EMEs experience surges. Conditional on a surge occurring, moreover, the magnitude of the capital inflow depends largely on domestic factors including the country's external financing needs, and the exchange rate regime. Finally, while similar factors explain asset- and liability-driven surges, the latter are more sensitive to global factors and contagion.

I. Introduction

After collapsing during the 2008 global financial crisis, capital flows to emerging market economies (EMEs) surged in late 2009 and 2010, raising both macroeconomic challenges and financial-stability concerns. By the second half of 2011, however, amidst a worsening global economic outlook, capital flows receded rapidly, eliminating much of the cumulated currency gains, and leaving EMEs grappling with sharply depreciating currencies in their wake.1 While such volatility is nothing new—historically, flows have been episodic (Figure 1)—it has reignited questions on the nature of capital flows to EMEs. What causes these sudden surges? What determines the allocation of flows across EMEs? And do foreign and domestic investors behave differently when making cross-border investment decisions? In this paper, we take up these issues; a companion paper (Ghosh et al., 2012) looks at why, when, and how capital flow surge episodes end.

Figure 1.
Figure 1.

Net Capital Flows to EMEs, 1980-2009

(in USD billions)

Citation: IMF Working Papers 2012, 022; 10.5089/9781463931841.001.A001

Source: IMF’s IFS database.

The literature on this subject has a long tradition of trying to identify global “push” and domestic “pull” factors in determining flows to recipient economies.2 Yet, in equilibrium, capital flows must reflect the confluence of supply and demand, so there must be both push (supply-side) and pull (demand-side) factors, and it is hard to attribute the observed flows to one side or the other. More meaningful, therefore, may be to consider the determinants of changes in capital flows, which might be associated with changes in supply factors (and declining costs of funds), or changes in demand factors (and rising costs of funds), or both (with roughly constant costs). Moreover, from a policy perspective, large changes in capital flows—surges—are of particular interest both because of their greater impact on the exchange rate and competitiveness, and because they are more likely to overwhelm the domestic regulatory framework, raising financial-stability risks. In this paper, we thus focus on surges, and examine what factors determine their occurrence as well as magnitude.3

While it is common to think of net inflows being the result of foreigners pouring money into the country (thereby increasing residents’ foreign liabilities), they could equally result from the asset side—residents selling their assets abroad or simply not purchasing as many foreign assets as before. Recent literature (Milesi-Ferretti and Tille, 2011; Forbes and Warnock, 2011) stresses the need to distinguish between these cases to better understand cross-border capital movements—especially in advanced economies, where gross flows of assets and liabilities dominate the net movements. Though less true of emerging markets (where net capital flows still largely reflect changes in external liabilities), the distinction may nevertheless be worth making, as liability-driven inflow surges might have different properties from asset-driven surges, and thus call for different policy responses. For example, it seems plausible that domestic investors would be more responsive to changes in local conditions because of informational advantages, while foreign investors may be more sensitive to global conditions. If so, and associating asset-driven surges with the investment decisions of domestic residents, and liability-driven surges with those of foreigners, there would be corresponding implications for the different types of surges.

In this paper, therefore, we also differentiate between asset- and liability-driven surges, and compare their determinants. We do so by first identifying surges in net capital flows, and then classifying the surge according to whether it corresponds mainly to changes in the country’s foreign asset or liability position.4 In addition, while earlier studies have often focused on a selected set of push and pull factors—typically ignoring the real domestic interest rate and/or the country’s external financing needs—we systematically account for the plausible drivers of surges, including the return differential (adjusted for expected changes in the exchange rate), measures of risk in global markets, as well as the macroeconomic and structural characteristics, and the external financing needs of the recipient country. A key innovation of our study in this regard is to use an intertemporal optimizing model of the current account to proxy for the country’s external financing needs.

We begin our empirical analysis by developing simple algorithms to identify surge episodes in 56 EMEs over 1980–2009. We employ two methods: a “threshold” approach—net flows (in percent of GDP) that fall in the top 30th percentile of the country’s own, and the entire sample’s, observations; and a “clustering” approach that avoids imposing ad hoc thresholds, and uses statistical clustering techniques on (standardized) net flows to distinguish between surges, normal flows, and outflows. With these two methods, we identify 290 and 338 surges (around one-fourth of the panel), respectively, roughly synchronized in the early 1980s (prior to the onset of the Latin American debt crisis); the early 1990s (as these countries emerged from the debt crisis); and the mid-2000s, as capital flows to EMEs recovered from the Asian crisis and the Russian default, and then accelerated in the run-up to the global financial crisis.

The very synchronicity of surge episodes across countries suggests that global factors might be at play. Indeed, we find this to be the case—global factors, including US interest rates, and global risk aversion (as captured by the volatility of the S&P 500 index)—are key determinants of whether inflow surges to EMEs will occur. At the same time, whether a particular EME experiences a surge also depends on its own attractiveness as an investment destination. Fundamentals, including external financing needs implied by the consumption smoothing optimal current account deficit, financial openness and interconnectedness, real economic growth, and institutional quality also help determine the likelihood that the country experiences an inflow surge. Conditional on the surge occurring, moreover, domestic “pull” factors, including the country’s growth rate, external financing needs, and exchange rate regime, are important in determining its magnitude. Broadly speaking, therefore, surges in capital flows to EMEs are driven by global push factors—but where they end up depends equally on domestic pull factors, which explains why not all countries experience a surge when aggregate flows toward EMEs rise sharply.

Our analysis also shows that inflow surges to EMEs are mainly liability-driven—only one-third of the net flow surges correspond to changes in residents’ foreign asset transactions. The factors driving the two types of surges turn out to be quite similar: global factors matter for both, with lower US interest rates and greater risk appetite encouraging both foreigners to invest more in EMEs, and domestic residents to invest less abroad. Yet some differences are discernible. Foreign investors are equally attuned to local conditions as domestic investors, but tend to be more sensitive to changes in the real US interest rate and global risk, and are also more subject to regional contagion than asset-driven surges. These conclusions are reaffirmed from a binary recursive tree analysis, which shows that global factors, specifically, global risk, play a key role in driving large foreign inflows to EMEs.

Our findings, which are robust to different estimation methodologies, surge identification algorithms, and model specifications, hold important policy implications. Inasmuch as surges reflect exogenous supply-side factors that could reverse abruptly, or are driven by contagion rather than fundamentals, the case for imposing capital controls (provided macro policy prerequisites have been met; Ostry et al., 2011) on inflow surges that may cause economic or financial disruption—and for greater policy coordination between source and recipient countries—is correspondingly stronger. If the aggregate volume of capital flows to EMEs is largely determined by supply-side factors, but the allocation of flows across countries depends on local factors (including capital account openness), there may also be a need for coordination among recipient countries to ensure that they do not pursue beggar-thy-neighbor policies in an effort to deflect unwanted surges to each other.

Our contribution to the existing literature is thus three-fold. First, we focus on surges of net capital flows, examining both why they occur, and the magnitude of the flows conditional on their occurrence. Second, we differentiate between asset- and liability-driven surges, and examine whether they react differently to changes in global and local conditions. Third, we systematically account for the plausible drivers of surges—including the return differential (adjusted for the expected exchange rate changes), and an important new proxy of the country’s external financing needs obtained from an intertemporal optimizing model of the current account—and complement our regression analysis with binary recursive trees.

The rest of the paper is organized as follows. Section II outlines our empirical strategy for investigating the determinants of surge occurrence and magnitude. Section III describes how we identify inflow surges, and documents the key features associated with surge episodes. Section IV presents the main empirical results and sensitivity analysis. Section V further explores the drivers of inflow surges using binary recursive trees. Section VI concludes.

II. Empirical Strategy

Growing financial integration over the past few decades, together with the evident volatility of capital flows, has spawned a voluminous literature on the determinants of cross-border capital flows. While early empirical studies paid particular attention to the role of interest rate differentials (for example, Branson, 1968; and Kouri and Porter, 1974), later studies have characterized the determinants into “push” and “pull” factors, and focused more on evaluating the relative importance of each (for example, Chuhan et al., 1993; Fernandez-Arias, 1996; Fernandez-Arias and Montiel, 1996; Taylor and Sarno, 1997).5 Push factors reflect external conditions (or supply-side factors) that induce investors to increase exposure to EMEs—for example, lower interest rates and weak economic performance in advanced economies, lower risk aversion, and booming commodity prices. Pull factors are recipient country characteristics (or demand-side factors) that affect risks and returns to investors, and depend on local macroeconomic fundamentals, official policies, and market imperfections.6

Since, in equilibrium, flows must reflect the confluence of supply and demand, it is not surprising that most studies of the level of capital flows find that both push (supply-side) and pull (demand-side) factors matter (see, for example, Chuhan et al., 1993; Taylor and Sarno, 1997; Griffin et al., 2004; IMF, 2011; Fratzscher, 2011).7 But those that look at the change in capital flows present a more mixed picture. Calvo et al. (1993) and Fernandez-Arias (1996) find a dominant role of global factors, notably US interest rates, in driving capital flows to Latin America and Asia in the early 1990s, while, for a similar sample, Taylor and Sarno (1997) find that US interest rates and domestic credit worthiness are equally important for changes in equity flows, but that US interest rates are much more important in driving the short-run dynamics of bond flows.

And what about surges? The dynamics and determinants of these (exceptionally large) capital flows may be quite different from more normal variations, but existing empirical evidence is scant.8 The few available studies (Reinhart and Reinhart, 2008; Cardarelli et al., 2009) simply present some stylized facts about the association of net flow surges with global factors such as US interest rates, world output growth, and commodity prices, as well as with local characteristics, notably the current account deficit and real GDP growth. Looking at gross flows, Forbes and Warnock (2011) find that global risk, global liquidity, and global as well as domestic real growth matter for inflow surges, but find no role of advanced economy interest rates.9 They show, however, that the retrenchment of residents’ assets abroad is (positively) related with interest rates in advanced economies, and with global risk and contagion effects (through trade and financial channels).

Building on these various strands of the literature, we model both the likelihood of inflow surges (as defined in Section III below), and their magnitude (conditional on occurrence), as functions of: (i) the return differential, rjtd

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; (ii) global push factors, xt; (iii) domestic pull factors, zjt; and (iv) contagion, cjt:

Pr(Sjt=1)=F(rjtdα1+xtβ1+zjtγ1+cjtδ1)(1)
Kjt|sjt=1=rjtdα2+xtβ2+zjtγ2+cjtδ2+εjt(2)

where Sjt is an indicator variable of whether a surge in net capital flows (to GDP) occurs in country j in period t; Kjt|sjt = 1 is the magnitude of the net capital flow (to GDP) conditional on the surge, and where F(.) is assumed to follow the standard normal cumulative distribution function so (1) can be estimated by probit, and (2) can be estimated by Ordinary Least Squares. To address the potential endogeneity concerns of the domestic pull factors in both (1) and (2), we substitute contemporaneous values of these variables by their lagged values.10 Since many of the structural variables (for example, capital account openness) change only slowly, and because we are interested in the effect of global factors that will be common across recipient countries (for example, US interest rates), we do not include country for annual fixed effects, but control for region-specific effects and a range of country characteristics.11

Rate of return differential

The neoclassical theory predicts that capital should respond to interest rate differentials between countries—with capital flowing from countries with low return (capital-abundant advanced economies) to those with high return (capital-scarce emerging economies). The nominal interest rate differential is given by the standard uncovered interest rate parity condition:

ijtd=ijt-(it*+(ejt+1-ejt))(3)

where ijtd

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is the interest differential for country j at time t, ijt is the domestic interest rate (money market rate or treasury bill rate, according to data availability) of the emerging economy, it*
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is the advanced economy interest rate (proxied by the US 3-month treasury bill rate), and ejt is the log nominal exchange rate (an increase in ejt represents a deprecation). Subtracting the inflation rate from both sides of (3):

rjtd=ijt-(pjt+1-pjt)-{it*-(pt+1*-pt*)+(pt+1*+ejt+1-pjt+1)-(pt*+ejt-pjt)}(4)

or

rjtd=rjt-rt*-Δqjt+1e(5)

where rjtd

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is the real interest rate differential; pt and pt*
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are the log domestic and US price levels, respectively; rjt and rt*
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are the domestic and US real interest rates, respectively; and Δqjt+1e
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is the expected real exchange rate depreciation. We proxy for the expected real depreciation by the log difference between the current real effective exchange rate and its long-term trend (i.e., the implied overvaluation), Δqjt+1e=q~j-qjt
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, so capital flows to EMEs should respond positively to the differential:

rjtd=rjt-(q~j-qjt)-rt*(6)

Using (6)—that is, working in terms of the real interest rate differential—is useful because some of the EME observations include high- or even hyperinflationary periods. In the empirical results below, we present two estimates of (1) and (2). The first variant (the “constrained” model) includes the real-interest rate differential as defined in (6), so that the coefficients on the individual terms (rjt, rt*

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and Δqjt+1e
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) are constrained to be equal. The second variant (the “unconstrained” model) includes the terms individually so the coefficients are unrestricted, which allows to identify whether the effect of the real interest rate differential stems mainly from the push (rt*
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) or pull (rjt and (qj-q~)
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) factors.

Global push factors

Global push factors reflect external conditions, largely beyond the control of EMEs, which underpin the supply of global liquidity. In addition to the real US interest rate (in the unconstrained model), we include the volatility of the Standard & Poor (S&P) 500 index, and world commodity prices as other global push factors.12 Higher US interest rates (proxying the rate of return in advanced economies) are expected to reduce capital flows to EMEs. Likewise, greater volatility of the S&P 500 index—as a measure of global market uncertainty—is likely to reduce the surge probability for EMEs since advanced economies are traditionally considered to be safe havens in times of increased uncertainty. Higher commodity prices (measured as the log difference between the actual and trend commodity price index to capture the effect of large movements in commodity prices) are likely to be positively correlated with inflow surges inasmuch as they indicate a boom in demand for EME exports, and perhaps the recycling of income earned by commodity exporters.

Domestic Pull Factors

For capital to flow, there must be corresponding investment opportunities in the destination country. Early studies of private capital flows to developing countries often included the country’s current account deficit as a measure of its financing needs (Kouri and Porter, 1974). But with the increasing importance of private (as opposed to official) flows to EMEs, this becomes almost tautological: abstracting from changes in reserves, the current account deficit must be (largely) financed by private capital flows, and the observed flows must correspond to the current account deficit.

To get around this problem, and to see whether capital flows to EMEs respond to “fundamentals,” we turn to an intertemporal optimizing model of the current account (Ghosh, 1995). In such a model, the capital inflow corresponding to the optimal current account deficit (CADt*

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) can be shown to equal the present discounted value of expected changes in national cash flow—or the difference between GDP (Qt), investment (It), and government consumption (Gt):13

CADt*=Σj=1E{Δ(Qt+j-It+j-Gt+j)}(1+r)j(7)

According to the consumption-smoothing model (7), the country has an external financing need (that is, optimally, a current account deficit) when output is temporarily low, and/or government consumption and investment are temporarily high (for example, in the face of a positive productivity shock). Permanent shocks, of course, have no impact on the (consumption-smoothing component of the) current account as the country should adjust to such shocks. Since surges are episodes of temporarily high capital inflows, they presumably correspond to temporary shocks to the domestic economy. Accordingly, CADt*

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, as defined in (7), should be a good proxy for the country’s external financing needs that are met by surges in net capital flows.

Even if the country does have an external financing need, it may not be met if the capital account is closed (indeed, the derivation of (7) assumes perfect capital mobility). To capture this possibility, we include a measure of (de jure) capital account openness in (1) and (2), which is taken from Chinn and Ito (2008), and is based on the IMF’s Annual Report on Exchange Arrangements and Exchange Restrictions (AREAR). Countries that are more financially open are in principle more likely to experience a surge of capital inflows than relatively closed economies. Regardless of de jure openness, however, a country (sovereign) that is in arrears or otherwise in default on its external payments is unlikely to be an attractive destination for foreign investors and is less likely to experience an inflow surge. We therefore also include a dummy variable (based on Reinhart and Reinhart, 2008) to capture whether the sovereign is in a debt crisis such that it is unable to make its principal or interest payments by the due date.

Fast growing economies are more likely to experience large capital flows, not only because of their potentially large financing needs, but also because of their greater potential productivity and returns, as are countries with better institutional quality (Alfaro et al., 2008). Thus, we include real GDP growth rate as well as a measure of institutional quality among the pull factors. We also include the de facto exchange rate regime (taken from the IMF’s AREAER) to capture the possibility that the implicit guarantee of a fixed exchange rate may encourage greater cross-border borrowing and lending. Countries that are better integrated with global financial markets may be more likely to receive inflows (for example, as in Ghosh et al., 2011; and Hale, 2011)—perhaps because of lower informational costs for foreign investors or because of more diversified sources of external financing. Therefore, we also include a measure of the country’s financial “connectedness” as proxied by its centrality in the global banking network (specifically, by the proportion of advanced economies that have banks with cross-border exposure to the recipient country; Minoiu and Rey, 2011). Finally, in the unconstrained model, we include the domestic real interest rate (which should be positively correlated with surges), and the estimated overvaluation of the currency (which should be negatively correlated) as separate terms based on (6).

Contagion

Another external factor, which has gained much attention in recent years, is contagion. Recent literature finds a strong effect of contagion, particularly in the context of economic and financial crises/sudden stops (for example, Glick and Rose, 1999; Kaminsky et al., 2001; Forbes and Warnock, 2011), and identifies several channels (trade, financial, geographic location, or similar economic characteristics) through which contagion may occur.14 To capture the impact of contagion on surge likelihood, we include in (1) a regional contagion variable defined as the proportion of other countries in the region experiencing a net capital flow surge (and, correspondingly, in the magnitude regression (2) we include the average net flow (in percent of GDP) to other countries in the region experiencing a surge).15

III. Identifying Surges

A. Methodology

Our starting point for the empirical analysis is to identify inflow surges. A common approach in the literature is to use thresholds—for example, Reinhart and Reinhart (2008) select a cutoff of 20th percentile across countries of total net capital flows (in percent of GDP), and Cardarelli et al. (2009) define a surge when net private capital flows (again in proportion to GDP) for a country exceed its trend by one standard deviation (or falls in the top quartile of the regional distribution). In recent work, Forbes and Warnock (2011) use quarterly data on gross capital flows, and define a surge as an annual increase in gross inflows that is more than one standard deviation above the (five-year rolling) average and where the increase is at least two standard deviations above the average in at least one quarter.16

There are pros and cons to defining surges in terms of net or gross inflows. On the one hand, some financial stability risks, such as foreign currency exposure of unhedged domestic borrowers, may depend on the country’s gross external liabilities, and as argued above, the dynamics of liabilities may be quite different from those of assets. On the other hand, most macroeconomic consequences of capital flows (such as exchange rate appreciation or macroeconomic overheating) and some financial-stability risks, will be related to net, not gross, flows. Moreover, for EMEs, net capital flows mostly correspond to changes in liabilities, with relatively little action on the asset side.17 Indeed, the problem with using gross flows is that many of the identified “surges” may not constitute periods of net flows, let alone exceptionally large net flows. In this paper, therefore, we define surges in terms of the net flow of capital but use gross flow data to distinguish between those that correspond mainly to changes in external liabilities and those that correspond to changes in assets.

We obtain data from the IMF’s Balance of Payment Statistics, and define net capital flows as total net flows excluding “other investment liabilities of the general government” (which are typically official loans) and exceptional financing items (reserve assets and use of IMF credit), expressed in percent of GDP. We identify surges using two methods. The first approach, which follows the existing literature, is to define a surge as any year in which net capital flows exceed some threshold value. We set the threshold at the top 30th percentile for the country, provided the net flow (expressed in percent of GDP) also falls in the top 30th percentile for the entire (cross-country) sample. This ensures that observations of net flows that are large by (country-specific) historical as well as international standards are included as surges. Likewise, observations in the bottom 30th percentile (of the country-specific as well as the full sample’s distribution) are coded as outflows; all other observations are coded as “normal” flows.

Our second approach is more novel and avoids imposing ad hoc thresholds. We apply statistical clustering techniques (specifically, k-means clustering) to group each country’s observations on (standardized) net flows such that the within-cluster sum of squared differences from the mean is minimized (while the between-cluster difference in means is maximized). As a result, each observation belongs to the cluster (or group) with the nearest mean, and clusters comprise observations that are statistically similar. Using this technique, we group each country’s data into three clusters that we identify with: (i) surges; (ii) normal flows; and (iii) outflows. In both approaches, we group consecutive surge observations to form a surge episode provided they are not interrupted by a year of normal flows or outflows.

While the particular choice of algorithm to identify surges inevitably involves trade-offs, our approaches have the advantage of ensuring uniform treatment across countries while still allowing significant cross-country variation in the absolute threshold of a surge.18 The use of two, wholly independent approaches also gives confidence about the robustness of the obtained results. As with other empirical studies, however, dating the beginning and the end of surges is not always clear cut since the strict application of any algorithm to identify surges runs the risk of omitting at least some observations of relatively large net capital flows that may otherwise be part of an episode. We therefore also construct a one-year window around the identified episodes, including the immediate pre- and post-surge years (provided the net capital flow is positive in these years), and check the robustness of our estimation results to these extended episodes.

B. Key Features

We apply the threshold and cluster approaches to a sample of 56 EMEs using annual data for the period 1980–2009.19 There is considerable overlap in the resulting surge observations, with a correlation of about 0.8 between them, although the threshold approach yields somewhat fewer, but larger, surges.20 For example, Figure B1 shows the identified surge observations for Colombia using the two approaches. There are 3 observations of net capital flow to GDP for Colombia that are in the top 30th percentile of the country-specific distribution, as well as in the top 30th percentile of the overall distribution of net capital flows to GDP, and hence are coded as surges under the threshold approach. Through the cluster analysis, however, we obtain 9 surge observations, half of which are large from the country’s historical (but not from a global) perspective. In what follows, we focus on the results when surges are defined using the threshold approach, which are more extreme as they reflect both country-specificity and international uniqueness, reserving the cluster-identified surges for our robustness checks.

Under the threshold approach, we obtain 290 surge observations (which yield 149 surge episodes), the majority of which are in Eastern Europe and Latin America. The average duration of each episode is around 2 years, while the average net capital flow during the episode is about 10 percent of GDP. As a proportion of GDP, the largest surges are actually in the Middle East and African countries (around 12 percent of GDP, perhaps because of large resource extraction investment projects), followed by emerging Europe. Surges have become more frequent in recent years with the share of surge observations rising from about 10 percent in the 1980s to more than 20 percent in the 1990s, and to almost 30 percent in the last decade (Figure 2). Similar patterns are obtained when cluster analysis is applied—338 surge observations (grouped into 168 surge episodes) are identified, most of which coincide with those from the threshold approach.

Figure 2.
Figure 2.

Surges of Net Capital Flows to EMEs, 1980-2009

Citation: IMF Working Papers 2012, 022; 10.5089/9781463931841.001.A001

Source: Authors’ estimates based on IFS.

Classifying by the type of surge shows that the majority (more than two-thirds) are driven by an increase in residents’ liabilities (liability-driven) rather than by a decline in the holdings of their assets abroad (asset-driven).21 Asset-driven surges outnumber liability-driven surges in only two out of the 30 years of our sample—1982 and 2008, both of which are crisis years (Figure 3, panel a).22 Moreover, looking at surge episodes, nearly all begin with a liability-driven surge, suggesting that as foreign investment starts flowing into an EME, domestic investors follow suit, repatriating foreign assets in order to invest at home. On average, liability-driven surges are also somewhat larger than asset-driven surges, though the difference is not statistically significant (Figure 3, panel b).

Figure 3.
Figure 3.

Types of Surges, 1980–2009

Citation: IMF Working Papers 2012, 022; 10.5089/9781463931841.001.A001

Source: Authors’ estimates.

An initial snapshot of the occurrence and magnitude of inflow surges suggests three noteworthy points. First, surges seem to be synchronized internationally (Figure B2), generally corresponding to “well-established” periods of high global capital mobility—the early 1980s (just before the Latin American debt crisis), the mid-1990s (before the East Asian financial crisis and Russian default), and the mid-2000s in the run-up to the recent financial crisis—suggesting that common factors are at play. Second, even in times of such global surges, not all EMEs are affected. In fact, the proportion of EMEs experiencing an inflow surge in any given year never exceeds one-half of the sample, with some countries experiencing them repeatedly. As such, conditions in the recipient countries must also be relevant. Third, there is considerable time-series and cross-sectional variation in the magnitude of flows conditional on the occurrence of a surge. For example, Asian countries experienced the largest surges (in proportion of GDP) during the 1990s wave of capital flows, whereas emerging Europe experienced the largest surges in the mid-2000s wave. Thus, both global and domestic factors appear to be relevant in determining surges—perhaps global factors driving the overall volume of flows to EMEs, and domestic factors influencing their allocation.

What are these factors? A simple tabulation of explanatory variables during surge, normal, and outflow periods suggests a number of global push and domestic pull factors may be relevant (Table 1). During surge periods, the US real interest rate and global market uncertainty (S&P 500 index volatility) are lower, while commodity prices are higher, than at other (normal or outflows) times. Turning to domestic factors, when experiencing surges, recipient countries tend to have larger external financing needs, and faster output growth, as well as more open current and capital accounts (with greater financial interconnectedness), and stronger institutions.

Table 1.

Summary Statistics of Selected Variables

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Source: Authors’ calculations.

Observations restricted to the estimated sample as in Table 2. Real domestic interest rate and real GDP growth rate have been re-scaled using the formula x/(1+x) if x≥0, and x/(1-x) if x<0 to transform the outliers. *** indicates significant difference between the surge and non-surge observations at the 1 percent level.

IV. Estimation Results

The statistics reported in Table 1 are suggestive of the factors that might determine when and whether a country experiences an inflow surge. In what follows, we examine more formally the determinants of the occurrence and magnitude of surges. Below, we also split surges according to whether they are asset or liability-driven and conduct various robustness checks on our results.

A. Occurrence of Surges

We begin by estimating the “constrained” variant of the surge occurrence probit model specified in (1), where the real interest rate differential (adjusted for the expected real exchange rate depreciation) enters as a single composite variable (Table 2, cols. [1]-[5]). According to the estimates, a higher real interest rate differential raises the likelihood of an inflow surge, though the coefficient only becomes statistically significant when domestic pull factors are taken into account. Greater global market uncertainty (volatility of the S&P 500 index) has a strong dampening effect on the probability of a surge of capital to EMEs, presumably because—at least traditionally—these countries have not been viewed as safe havens at times of heightened uncertainty and risk aversion. Conversely, commodity price booms, which likely signal higher global demand for EME exports, are positively correlated with inflow surges, as is regional contagion (though the latter becomes statistically insignificant after controlling for domestic pull factors). Although individual coefficients are highly statistically significant, these global factors have limited explanatory power: the pseudo-R2 (which compares the log likelihood of the full model with that of a constant only model) is 8 percent, and the probit sensitivity (proportion of surges correctly called) is about 14 percent.

Table 2.

Likelihood of Surge, 1980-2009

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Source: Authors’ estimates.Notes: Dependent variable is a binary variable equal to 1 if a surge occurs and 0 otherwise. Constrained model refers to the specification where real interest rate differential between country i and the US (real domestic interest rate-real US interest rate-REER overvaluation) is included. All regressions are estimated using a probit model, with clustered standard errors (at the country level) reported in parentheses. Constant and region-specific effects are included in all specifications. ***,**,* indicate significance at 1, 5, and 10 percent levels, respectively. Sensitivity (specificity) gives the fraction of surge (no-surge) observations that are correctly specified. All variables except for global factors (real US interest rate, S&P 500 index volatility, and commodity price index), regional contagion, and financial interconnectedness are lagged one period.

Turning to domestic pull factors, the external financing need implied by the optimal consumption-smoothing current account is highly significant as is real GDP growth in the recipient country. Countries with fewer capital account restrictions, that are better connected (in the sense of more sources of cross-border loans), or that have stronger institutions are also significantly more likely to experience inflow surges. Countries with more flexible exchange rate regimes or that are in default are less likely to experience inflow surges, though neither variable is statistically significant. Adding these pull factors more than doubles the pseudo-R2 to 20 percent and raises the sensitivity to 27 percent.

The right-hand panel of Table 2 (cols. [6]-[10]) reports the corresponding estimates when the real interest rate differential is not constrained to enter as a single term so that the US real interest rate, domestic real interest rate, and estimated real exchange rate overvaluation enter separately. Doing so shows that much of the effect of the real interest rate differential is through the US real interest rate: evaluated at the mean of other explanatory variables, a 100 basis point rise in US real interest rates would lower the likelihood of an inflow surge by 3 percentage points (where the unconditional probability of a surge in the estimated sample is 22 percent). Real exchange rate overvaluation lowers the estimated likelihood of a surge, though the coefficient is not statistically significant when the full set of domestic factors is added (Table 2, cols. [9]-[10]), while the domestic real interest rate, though positive, is not statistically significant in any of the specifications. Most of the other variables are of similar magnitude and statistical significance to those estimated under the restricted variant. Overall, the model correctly calls some 80 percent of the observations, and almost 30 percent of the surge observations, with a pseudo-R2 of 21 percent.

To put the estimated effects in perspective, Figure 4 plots the implied probability of a surge evaluated around the means of the explanatory variables based on the estimates reported in column (10). Against an unconditional probability of 22 percent, a one standard deviation shock to the volatility of the S&P 500 index lowers the predicted surge probability by about 3 percentage points, while the corresponding shock to the commodity price index raises the surge probability by about 7 percentage points. Turning to domestic macroeconomic factors, a one percentage point increase in the country’s real GDP growth rate, or a one percent of GDP increase in its external financing needs, raises the predicted likelihood of a surge by about 1 and 3 percentage points, respectively. On capital account openness and the institutional quality index, moving from the sample median to the 75th percentile raises the predicted probability of a surge by some 4 to 5 percentage points, respectively.

Figure 4.
Figure 4.

Predicted Probabilities of Surge Occurrence

Citation: IMF Working Papers 2012, 022; 10.5089/9781463931841.001.A001

Source: Authors’ estimates.Notes: Predicted probabilities are based on the estimation results reported in Table 2 (column 10) holding all other variables fixed at mean value.

B. Magnitude of Flows in Surges

The probit estimates above give the likelihood of experiencing an inflow surge, but the magnitude of the capital flow during a surge also varies considerably (ranging from 4 percent of GDP to about 54 percent of GDP as shown in Table 1). Is it possible to say anything about the size of the surge conditional on its occurrence? Table 3 reports the estimation results for the surge magnitude regression (2), where the dependent variable is net capital flow (expressed as a proportion of GDP), and the sample comprises only the surge observations.

Table 3.

Magnitude of Surge, 1980-2009

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Source: Authors’ estimates.Notes: Dependent variable is net capital flow to GDP if a surge occurs. Constrained model refers to the specification where real interest rate differential between country i and the US (real domestic interest rate-real US interest rate-REER overvaluation) is included. All regressions estimated using pooled OLS. Constant, and regional specific effects are included in all specifications. All variables except for global factors (real US interest rate, S&P 500 index volatility, and commodity price index), regional contagion, and financial interconnectedness are lagged one period. Clustered standard errors (at the country level) reported in parentheses. ***,**,* indicate significance at 1, 5, and 10 percent levels, respectively.

Again, we present the constrained model in which the real interest rate differential enters the regression as a single term, and the unconstrained model where the US real interest rate, domestic real interest rate, and overvaluation are allowed their own coefficients. The real interest rate differential is statistically insignificant, while the global factors appear to play a more limited role. A 100 basis point decline in the real US interest rate is associated with almost 1 percent of GDP larger capital flows (Table 3, cols. [7]-[12]), but commodity price booms and S&P 500 index volatility have mostly insignificant effects, suggesting that these factors act largely as “gatekeepers”—capital surges toward EMEs only when these global conditions permit, but once this hurdle is passed, the volume of capital that flows is largely independent of it. An interesting finding is that of a negative (and in the unconstrained model, statistically significant) effect of the regional contagion variable, which most likely indicates that an increase in the average flow to other countries in the region implies less capital left to be allocated to the country in question.

Since countries that experience a surge already share the macroeconomic and structural characteristics identified above, several of the domestic pull factors are statistically insignificant. Nevertheless, the nominal exchange rate regime, real exchange rate overvaluation, and external financing needs of the country are all highly statistically significant. A one-percent of GDP increase in the estimated external financing need is associated with one-third of one percent of GDP higher capital inflows, while 10 percent overvaluation of the real exchange rate is associated with about 2 percent of GDP lower net capital flows. Other factors equal, a country with a pegged exchange rate would experience 3 percent of GDP larger capital flows during a surge than if it had a more flexible exchange rate regime. Finally, countries with more open capital accounts appear to experience larger surges: moving from the 25th percentile of the sample’s capital account openness index to the 75th percentile is associated with 1 percent of GDP higher capital inflows during a surge.

Overall, these findings are consistent with, but go beyond, the results of previous studies, and help to explain the stylized facts noted in Section III. Specifically, the finding that the likelihood of surge occurrence is influenced strongly by global factors—notably, the US interest rates, as argued by Calvo, Liederman and Reinhart (1993) and Reinhart and Reinhart (2008), and global risk—explains the synchronicity of surges across regions, and highlights that sudden changes in these factors could trigger large swings in capital flows. Certain macroeconomic (in particular, growth performance and the external financing need), and structural characteristics (notably, financial openness and institutional quality), are also important for a surge to occur, which explains why not all countries experience a surge when in aggregate capital is flowing toward EMEs. Further, among the countries that experience a surge, the magnitude of the flow appears to be driven not only by the real US interest rate and external financing need, but also by the exchange rate regime and financial openness, with countries that have less flexible exchange rate regimes or those that are more financially open experiencing larger surges.

C. Asset- vs. Liability-Driven Surges

Does the nature of the surge matter? In other words, are the global and domestic factors identified above equally important for surges that mainly reflect changes in residents’ assets (asset-driven surges) and those caused by changes in their liabilities (liability-driven surges)? To examine this question, we re-estimate (1) and (2), but define the surge as being either asset or liability-driven.23 The top panel in Tables 4 and 5 presents the results for the constrained model for the two types of surges, while the bottom panel presents estimates for the unrestricted model with the real US and domestic interest rates and real exchange rate overvaluation included as separate terms.24

Table 4.

Likelihood of Surge: by Surge Type, 1980-2009

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Source: Authors’ estimates.Notes: Dependent variable is a binary variable (=1 if a surge occurs; 0 otherwise). Asset- (liability-) driven surge is defined as the surge when change in residents’ assets is larger than the change in their liabilities (assets). Regressions estimated using probit model, with clustered standard errors (at the country level) reported in parentheses. All variables except for real US interest rate, S&P500 index volatility, commodity price index, regional contagion and financial interconnectedness are lagged one period. Constant and region-specific effects are included in all specifications. ***,**,* indicate significance at 1, 5, and 10 percent levels, respectively.
Table 5.

Magnitude of Surge: by Surge Type, 1980-2009

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Source: Authors’ estimates.Notes: Dependent variable is net capital flow to GDP if a surge occurs. Asset- (liability-) driven surge is the surge when change in residents’ assets- (liabilities-) is larger than the change in residents’ liabilities (assets). All variables except for real US interest rate, S&P500 index volatility, commodity price index, regional contagion and financial interconnectedness are lagged one period. Constant, and region specific effects are included. All regressions estimated using OLS. Clustered standard errors (at the country level) reported in parentheses. ***,**,* indicate significance at 1, 5, and 10 percent levels, respectively.

The results for the probit model show that the real interest rate differential raises the likelihood of both asset and liability-driven surges, but the impact is statistically significant for the latter only (Table 4, columns [5]-[10]). Increased global market uncertainty however matters strongly for both types of surges, such that in times of increased global market uncertainty, foreign as well as domestic investors exit EMEs and prefer to invest in safe haven countries.25 Nevertheless, foreign investors appear to be more sensitive to global market uncertainty: a one standard deviation shock to the S&P 500 index volatility reduces the likelihood of a liability-driven surge by 4 percentage points compared to 1 percentage point for asset-driven surges. Asset-driven surges are more likely when commodity prices are booming—whereas commodity prices have no discernible impact on liability-driven surges. By contrast, liability-driven surges are more subject to regional contagion.

Among the domestic pull factors, the external financing need, real economic growth, capital account openness, and institutional quality matter for both types of surges. Liability-driven surges, however, are more sensitive to the recipient country’s external financing need and economic growth prospects—such that a one percentage point increase (at mean values), raises the likelihood of a liability-driven surge by about an additional 1 and 0.5 percentage point, respectively, as compared to an asset-driven surge (Figure 5). The impact of capital account openness is somewhat similar on both types of surges—moving from the 25th percentile of the capital account openness index to the 75th percentile raises asset- and liability-driven surge probabilities by about 3 percentage points. The strong impact of capital account openness on asset-driven surges is intuitive as only when capital flows are liberalized (and can leave the national jurisdiction) in the first place, could they be retrenched from abroad and invested in the domestic economy. Financial interconnectedness, however, has a more pronounced impact on liability-driven surges—indicating that EMEs with greater financial linkages are more likely to experience large foreign capital flows.

Figure 5.
Figure 5.

Predicted Probabilities of Asset- and Liability-Driven Surge Occurrence

Citation: IMF Working Papers 2012, 022; 10.5089/9781463931841.001.A001

Source: Authors’ estimates.Notes: Predicted probabilities for asset- and liability-driven surges are based on estimation results in Table 4 (panel b, cols. 5 and 10, respectively) holding all other variables fixed at mean value.

The results of the unconstrained model show that real US interest rates matter significantly for the occurrence of both asset and liability-driven surges, though the impact is larger for the latter—a 100 basis points increase in the real US interest rate (evaluated at mean values) lowers the predicted probability of a liability-driven surge by about 2 percentage points, and that of an asset-driven surge by 1 percentage point. Interestingly, asset-driven surges appear to react more strongly to changes in the real domestic interest rate, while liability-driven surges respond more to expected changes in the exchange rate with greater real exchange rate overvaluation (and hence expected depreciation) making liability-driven surges less likely. For the other factors, the signs and magnitude of the estimated effects from the unconstrained model are very similar to those obtained above from the constrained model.

In terms of the magnitude of flows during surges, as before, several domestic macroeconomic and structural characteristics are statistically insignificant because by definition countries are sufficiently similar to have experienced a surge. Nevertheless, the results from the constrained model show that the real interest differential and other global factors are statistically insignificant for bother asset- and liability-driven surges. Domestic factors, however, do matter. The size of inflows received is larger if nominal exchange rate regimes are less flexible and capital accounts are more open (Table 5, panel a). Thus, a country with a pegged exchange rate experiences about 2 and 5 percent of GDP larger capital flows during asset- and liability driven surges, respectively, than if it had a more flexible exchange rate regime. Likewise, moving from the 25th percentile of the capital account openness index to the 75th percentile raises the size of the surge by about 1-2 percent of GDP for asset- and liability-driven surges. The external financing need and regional contagion, however, strongly impact the magnitude of liability-driven surges only such that larger external financing needs, and smaller inflows to other countries in the region imply larger surges. Moreover, the results from the unconstrained model (Table 5, panel b) show that real US interest rates, and real exchange rate overvaluation also strongly affect the magnitude of liability-driven surges—specifically, a 100 basis point increase in the real US interest rate, and a 10 percentage point real exchange rate overvaluation imply lower inflows by about 1 and 2 percent of GDP, respectively.

The results for both the occurrence and magnitude of surges suggest that while asset- and liability-driven surges have many common factors, there are also some important differences. In particular, liability-driven surges are more sensitive to global factors and to contagion, but are also more responsive to the external financing needs of the country and dependent on its financial interconnectedness. Inasmuch as liability-driven surges reflect the investment decisions of foreigners, these findings make intuitive sense.

D. Sensitivity Analysis

To check the robustness of our estimates reported above, we conduct a range of sensitivity tests below, which pertain to the dating and coverage of surge episodes, our alternative methodology for identifying surges (cluster analysis), model specification (alternative proxies and additional regressors), and sample period.

Extended episodes

Pinning down the exact timing (beginning and end) of surge episodes is not always straightforward. Thus, while our surge episodes largely overlap (for at least one year) with episodes identified in other studies (e.g., Reinhart and Reinhart, 2008; and Cardarelli et al., 2009), they do not coincide completely (nor do surge episodes identified in other studies correspond exactly with each other). In general, strict application of any algorithm to identify surges runs the risk of omitting at least some observations of relatively large net capital flows that in reality are probably part of the same episode but that do not quite meet the criteria.

To address these concerns, we construct a one-year window around the identified episode, including the year immediately before and immediately following the surge episode (provided the net private capital flow in those years is positive), and re-estimate all specifications using the extended surge variable.26 Tables 6 and 7 (col. [1]) present the estimation results for this exercise, which largely support the findings reported in Tables 2 and 3, respectively. Specifically, for surge likelihood, the US real interest rate, global market uncertainty, and commodity prices are all significant—while domestic factors such as the external financing need, real GDP growth rate, capital account openness, financial interconnectedness, and institutional quality are also strongly significantly. The main difference with the previous results is that the domestic real interest rate and the sovereign default dummy now become statistically significant. For surge magnitude, as before, the external financing need, a less flexible exchange rate regime, and expected real exchange rate change matter, as do the real US interest rate, and the amount of inflow received by other countries experiencing a surge in the region. There is also some evidence for extended episodes that commodity price booms lead to larger surges.

Table 6.

Likelihood of Surge: Sensitivity Analysis

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Source: Authors’ estimates.Notes: Dependent variable is a binary variable (=1 if a surge occurs; 0 otherwise). All regressions (except for complemetary log-log regression) are estimated using probit estimation method. Clustered standard errors (at the country level) are reported in parentheses. All variables except for real US interest rate, S&P500 index volatility, commodity price index, regional contagion, and financial interconnectedness are lagged one period. Constant and region specific effects are included in all specifications. ***,**,* indicate significance at 1, 5, and 10 percent levels, respectively. Extended=Surges identified using a one-year window (i.e., including the year before and after the surgeif the net capital flow is positive); Cluster=Surges identified using the cluster approach; Real US 10yr yield=Including the real US 10 yr government bond yield instead of the real US 3-month T-bill rate; RAI=including the (log of) Credit Suisse global risk appetite index (RAI) instead of the S&P 500 index volatility measure; VXO=Including the VXO index instead of the S&P 500 volatility index; Trade openness=Including trade to GDP ratio in the specification; Reserves to GDP=Including the stock of foreign reserves to GDP ratio in the specification; Stock market capitalization=Including stock market capitalization in the specification; Return on equity=Including banks’s return on equity in the specification; Private sector credit/GDP=Including private sector credit to GDP ratio in the specification; Trade links=Including trade links to measure contagion effects in the specification; Fixed effects=Including country fixed