Catalytic IMF? : a gross flows approach.
Date: 2016-01-15
Institutions: European Stability Mechanism
Subjects: International Monetary Fund, economic integration, financial market, international finance, monetary agreement, world economy

Working Paper Series  |  9 |  2015

Catalytic IMF? A gross flows approach

This paper argues that the strongest 
channel through which the IMF stabilises 
a borrowing country’s financial flows is via 
its effect on the domestic banking system.

Aitor  Erce 
European Stability Mechanism
Daniel Riera-Crichton
Bates College

Disclaimer
This Working Paper should not be reported as representing the views of the ESM.
The views expressed in this Working Paper are those of the author(s) and do not
necessarily represent those of the ESM or ESM policy.

Working Paper Series  |  9  |  2015

Catalytic IMF? A Gross Flows Approach1

AItor Erce2 European Stability Mechanism
Daniel Riera-Crichton3 Bates College

Abstract
The financial assistance the International Monetary Fund (IMF) provides is expected to catalyze private 
capital inflows. Such a catalytic effect has, however, proven empirically elusive. This paper deviates from 
the  standard  approach  based  on  the  net  capital  inflow  to  study  instead  the  IMF’s  catalytic  role  in  the 
context of gross capital flows. Using fixed-effects regressions, instrumental variables and local projection 
methods, we document dynamics that are absent from existing models of IMF catalysis. Our results show 
significant differences in how resident and foreign investors react to IMF programs. While IMF lending 
does not catalyze foreign capital, it does affect the behavior of resident investors, who are both less likely 
to place their savings abroad and more likely to repatriate their foreign assets. As domestic banks’ flows 
drive this effect, we conclude that IMF catalysis is “a banking story”. In comparing the effects across crisis 
types, we find that the effect of the IMF on resident investors is strongest during sovereign defaults, and 
that it exerts the least effect on foreign investors during bank crises.

Key words
IMF, catalysis, residence, capital flows

JEL codes
F32, F33, F36, G01, G15

¹We would like to thank A. Bonfiglioli, F. Broner, G. Cheng, A. Fernandez, P. Fioretti, N. Funke, A. Gilbert, J. Joyce and seminar 
participants at the European Stability Mechanism, International Risk Management Conference 2015, OeNB Conference on Macro-
Financial Linkages and Current Account Imbalances, Barcelona GSE Summer Forum 2015, LACEA 2015 and Wellesley College for their 
comments, M. de las Casas and X. Serra for sharing their dataset on IMF agreements, and S. Esarey for superb editorial assistance. 
These are the authors’ views and are not to be reported as those of the European Stability Mechanism.
2European Stability Mechanism, Luxembourg; E.mail: A.erce@esm.europa.eu.
3Bates College, Lewiston, Maine, US; E.mail: drieracr@bates.edu.

Disclaimer
This Working Paper should not be reported as representing the views of the ESM. The views expressed in this Working Paper are 
those of the author(s) and do not necessarily represent those of the ESM or ESM policy.
No responsibility or liability is accepted by the ESM in relation to the accuracy or completeness of the information, including any 
data sets, presented in this Working Paper.

© European Stability Mechanism, 2015 All rights reserved. Any reproduction, publication and reprint in the form of a 
different publication, whether printed or produced electronically, in whole or in part, is permitted only with the explicit written 
authorisation of the European Stability Mechanism.

ISSN 2443-5503 
ISBN 978-92-95085-15-2

doi:10.2852/436533 
EU catalog number DW-AB-15-010-EN-N

Introduction

Many crises feature, as part of the resolution strategy, the involvement of the
International Monetary Fund (IMF). In such cases, the Fund takes on the role
of an international lender of last resort and provides crisis-hit economies with
subsidized funding, provided those countries implement a macroeconomic ad-
justment program. The …nancial assistance is designed to give the economies
breathing space in which to solve their temporary …nancing problems.

This approach has both critics and supporters. The Fund itself defends this
strategy by arguing that it reassures private creditors that the exit from the
crisis will be orderly, reducing the potential for a drastic reaction (Cottarelli
and Giannini, 2002). An extensive literature has assessed the importance of
the so-called catalytic e¤ect of o¢ cial …nancing by looking at the net ‡ow of
capital entering/exiting countries under an IMF program. On the theoretical
front, Corsetti et al., (2006) and others have shown that IMF lending has the
potential to catalyze foreign capital in‡ows. Disconcertingly, from an empiri-
cal perspective, many studies cast doubt on the existence of any such positive
e¤ect. Critics have seized upon this lack of empirical evidence to argue that
Fund policies are too restrictive and generate moral hazard on both debtors
and creditors (Birds and Rowlands, 2002).1

In parallel, the literature on capital ‡ows has recently turned its focus to
the gross components of the …nancial account. According to this literature,
the gross ‡ows composing a country´s net capital in‡ow react dissimilarly to
di¤erent factors. Along these lines, Forbes and Warnock (2012) and Broner
et al. (2013) show that resident and foreign investors’ reaction functions are
distinct.2 These papers demonstrate that gross capital ‡ows are very large and
volatile, especially relative to net capital ‡ows. They also shed light on the
sources of ‡uctuations driving capital ‡ows by proving that crises can a¤ect
domestic and foreign agents asymmetrically.

In this paper, we bridge these two literature strands by looking at the cat-
alytic e¤ect of IMF lending through the lenses of the gross ‡ows composing the
current account. We distinguish varieties of capital ‡ows entering and exiting
an economy and study how they react to the signing of an IMF program. We
follow Broner et al. (2013) and separate ‡ows according to the investors’resi-
dence. Additionally, as in Janus and Riera-Crichton (2015), we study the e¤ect
of o¢ cial funding on a breakdown of capital ‡ows into and out of an economy,
regardless of the nationality of investors.

The contrasting experiences of Uruguay (2002) and Turkey (2005) after their
respective IMF programs were signed exemplify the relevance of our approach.
As shown in Graph 1, resident and foreign investors reacted markedly di¤erently
In Turkey, after the IMF agreement was signed, the
in these two instances.
In
foreign in‡ow continued unabated and residents retrenched only brie‡y.

1 IMF (2013) argues that the provision of o¢ cial …nancing can crowd out private …nancing,

potentially creating an anti-catalytic e¤ect.

2 This is also the case for inward and outward ‡ows (see Janus and Riera-Crichton, 2015).

1

contrast, in Uruguay, after the signing, signi…cant foreign capital took ‡ight.
Fortunately, residents’investment pattern also changed, cushioning the e¤ects of
the ‡ight; after the program signing, residents started to repatriate a signi…cant
amount of their savings abroad.

To obtain more systematic and robust evidence regarding the role of IMF
lending on gross capital ‡ows, we have compiled a detailed dataset of IMF
interventions and quarterly gross capital ‡ows for over 50 economies. We use it
to analyze whether IMF program signings have distinct e¤ects on gross ‡ows.
Non-random selection into o¢ cial support obscures the interpretation of the
relation between o¢ cial credit and gross capital ‡ows. We tackle this concern
by employing an instrumental variables approach. We follow Barro and Lee
(2005) and a large literature on the political and geo-strategic determinants
of IMF lending. This literature provides us with an easy and powerful way
of instrumenting o¢ cial support programs. Additionally, we use a linear local
projections method (Jorda, 2005) to gauge the dynamic reaction of the various
types of capital ‡ows to the enactment of IMF programs.

By documenting the contrasting e¤ect of IMF loans on resident and foreign
investors, a feature absent from standard models of IMF catalysis, this paper
informs this rich theoretical literature. Our results show signi…cant di¤erences
in how resident and foreign investors react to IMF programs. While the IMF
appears unable to catalyze foreign capital, substantial evidence shows that it
does a¤ect the behavior of resident investors. Remarkably, the strength of the
e¤ect of IMF loans on resident investors’behavior is such that we …nd evidence
of both more muted domestic capital ‡ight and an increased repatriation of
residents’ savings placed abroad. When we look at the reaction by types of
‡ows, we …nd that most of the catalytic e¤ect relates to domestic banking ‡ows,
making us conjecture that IMF catalysis is “a banking story”. Finally, using a
non-linear approach in our local projection estimates, we show that the type of
crisis the IMF program is …ghting a¤ects the degree to which both resident and
foreign investors react. The IMF appears to be better able catalyze domestic
savings during sovereign defaults and has the least e¤ect on foreign investors
during bank crises.

Next section frames our …ndings within the existing literature. Then, after
describing our dataset, we discuss the empirical approach and present our re-
sults. We conclude with a re‡ection on the potential drivers of our results and
their implications for the theoretical work on this area.

Catalytic IMF: A review of the literature

Defenders of the catalytic e¤ect argue that, by reassuring private creditors about
the existence of an ordered exit from a crisis, these interventions can stimulate
private ‡ows when most needed. A number of theoretical contributions support
this positive view. Corsetti et al. (2006) and Morris and Shin (2006) contend
that IMF lending is able to reduce the incidence of panic-driven liquidity crises.

2

Similarly, Peñalver (2004) shows that subsidized lending can induce the borrow-
ing country to exert e¤ort to avoid default. By raising future rates of return on
investment, o¢ cial lending encourages larger private capital ‡ows. De Resende
(2007) shows that if conditionality forces countries to save more, the resulting
lower probability of default can encourage private lenders to relax their bor-
rowing constraints. Instead, opponents argue that such policies generate moral
hazard both for debtors and creditors, and that the Fund’s seniority status is
detrimental to the debtor-creditor relationship as it might dilute private oblig-
In a framework of panic-driven liquidity runs, Zwart
ations (Saravia, 2013).
(2007) quali…es the results in Corsetti et al. (2006) by showing that catalysis
may not materialize given that, through its signaling e¤ect, IMF support can
trigger capital ‡ight.3

An extensive literature has studied empirically the signi…cance of the IMF´s
catalytic e¤ect, delivering at best mixed evidence. Critics have seized upon
this lack of empirical evidence to argue that an overestimation of its catalytic
role has led the Fund to impose excessively contractionary policies (Birds and
Rowlands, 2002). So far, the literature has focused on the current account and
speci…c categories of net capital ‡ows. A majority of studies, either regression
analyses or case studies, cast doubt on the existence of any such positive e¤ect
(Ghosh et al, 2002), although a catalytic e¤ect has been found in some circum-
stances.4 Mina and Martinez-Vazquez (2002), using aggregate country data,
…nd that IMF lending reduces the countries’reliance on short-term debt ‡ows.
Eichengreen and Mody (2003) …nd a stronger catalytic e¤ect for intermediate
economic fundamentals. Edwards (2003) …nds no catalytic e¤ect on bond is-
suance. The opposite is true for Mody and Saravia (2003), who …nd that larger
programs are associated with stronger catalysis and that a continued IMF pres-
ence in a country reinforces this e¤ect. However, an excessively lengthy presence
can be perceived as a sign of failure, discouraging capital ‡ows. Eichengreen et
al. (2005), who argue that the IMF’s role as a vigilante is more likely to manifest
in bond markets, …nd that, in high-debt countries, it is the size of assistance
that attracts private capital. Focusing on the volatility of net capital ‡ows,
Broto et al. (2011) show that the larger availability of Fund resources lowers
net ‡ows’ volatility. Saravia (2013) presents evidence on the relation between
IMF lending and countries’private and public debt maturity choices. Saravia
…nds that IMF loans reduce the maturity of new debt. He argues that this is
due to its senior status.

Data

Information on IMF interventions was collected from the IMF´s webpage and
various program reviews. While information is available from the 1950s, the
data used in this paper is restricted in two dimensions. First, we do not go back

3 Interestingly, none of these contributions distinguishes resident and foreign investors.
4 Various papers have tested whether access to capital markets response to IMF lending

di¤ers with the program design (see, for example, Erce, 2012).

3

beyond 1990 due to the scarcity of information on a su¢ ciently granular (quar-
terly) basis and we stop in 2008, given that the IMF changed both its Balance
of Payments methodology and portfolio of crisis resolution tools in 2009. Sec-
ond, we only focus on the IMF’s two traditional credit lines of crisis resolution
which are funded through its general resources: the IMF Stand-By Arrange-
ment (SBA) and the IMF Extended Fund Facility (EFF). The SBA, established
in 1952, is the IMF’s workhorse lending instrument for emerging and advanced
market countries. The SBA aims to help member countries address their short-
term balance of payments problems, emerge from crisis and restore sustainable
growth. The Extended Fund Facility (EFF) focuses on helping countries over-
come their medium/longer-terms balance of payments problems. This implies
a longer program engagement (up to 4 years instead of 3 under the SBA) and a
longer repayment period (up to 10 years instead of the 5 allowed for the SBA).
After this selection, we …nish with a sample of over 140 programs. The data
set includes the date of the arrangement, the actual date at which the program
…nished (end of the arrangement), the type and size of the program.5

The capital ‡ows data comes from the analytic presentation of the IMF’s
Balance of Payments Statistics Yearbooks (BOP). The IMF’s BOP dataset pro-
vides detailed disaggregated country-level data, on a quarterly basis since 1970.
This dataset allows us to construct various measures of Gross Capital Flows,
including by type of Flow. To understand the true catalytic e¤ect of assistance
programs into capital ‡ows we have to be careful in de…ning what is being “cat-
alyzed”. In this sense, just looking at a typical measure of Net Capital Flows
(i.e. changes of all liabilities –changes of all assets) could be misleading. To see
this, simply imagine that the impact of international assistance programs has
an asymmetric e¤ect on the behavior of domestic resident investors and foreign
investors; aggregating these, potentially opposite, e¤ects could hide the true na-
ture of these programs’catalyzing role. Furthermore, asymmetric e¤ects could
arise in the direction of the ‡ows regardless of the residency of the investors
or even among the di¤erent type of ‡ows. More importantly, from a policy
perspective, not all ‡ows are the same. The negative economic e¤ects of sud-
den net in‡ow reversals are well documented.6Additionally, shocks to speci…c
components of the net in‡ow, i.e. foreign investment reversals or domestic re-
trenchment could potentially lead to external crisis (Janus and Riera-Crichton,
2015). To capture this set of potential asymmetries, we use two di¤erent de-
compositions of the net in‡ow to create our gross measures of capital ‡ows. On
the one hand, we use the residence decomposition:

N et Inf lowit =

Liabilitiesit

Assetsit:

4

(cid:0) 4

This decomposition allows us to distinguish capital out‡ows by domestic

5 The data set also includes the original (programmed) expiration date. Actual and pro-
grammed end dates may di¤er either due to a program extension or an early cancellation.
Using these dates one could compute both the program duration on approval and actual du-
ration. Additional information includes the volume of funds …nally drawn under the program,
size and date of any program augmentation and information about its precautionary character.

6 See Calvo (2003) or Hutchinton and Noy (2006).

4

agents (COD), marked by changes in foreign assets held by domestic residents,
from capital in‡ows by foreigners (CIF), which are measured as changes in lia-
bilities of the reporting country’s residents held by foreign nationals (see Broner
et al. 2013).Alternatively, given that negative or positive values on the change
of assets and liabilities denote a speci…c direction of the capital ‡ow, we can
decompose the net in‡ow as:

N et Inf lowit = Gross Inf lowsit

Gross Outf lowsit =

(cid:0)
Liabilities(cid:0)it +

(
4

Assets+
it)

(1)

= (

Liabilities+

it +

Assets(cid:0)it)

4

4

(cid:0)
where now we aggregate ‡ows based on their direction (in/out) and not
on the residency of the transaction originators.7
In other words, in this ag-
gregation, gross in‡ows accumulate ‡ows of capital invested in the country by
foreign investors (
it) plus the capital domestic residents repatriate
Assets(cid:0)it), while gross out‡ows accumulate the purchase of foreign assets by
(
4
domestic investors (
it), plus the repatriation of capital from the domes-
tic economy by foreign nationals (

Liabilities+

Assets+

4

4

4

Liabilities(cid:0)it).

Using these two de…nitions of the net in‡ow, we build nine basic series of
gross capital ‡ows: Total Gross Flows, Capital Out‡ows by Residents, Capital
In‡ows by Foreigners, Total Private In‡ows, Total Private Out‡ows, and the
four individual components of de…nition (1).

4

Finally, our dataset includes additional variables that are used as controls,
either in the panel regressions and linear projections, or as instruments when
implementing our instrumental variables strategy. The controls include the High
Yield Index, the VIX index and the Federal Funds Rate obtained from DataS-
tream, and the Chinn-Ito Index of capital account liberalization and nominal
GDP growth from the World Economic Outlook database.

Empirical Analysis

In this section, we showcase the results of our empirical analysis on the e¤ects
of IMF …nancial assistance on the components of the net capital in‡ow. First,
we report some important stylized facts from our main variables. Then, we
turn to the regression analysis. We start with a simple least squares dummy
variable (LSDV) estimation approach with lagged independent variables. While
the lagged structure imposes some degree of causal direction in our regressions,
we are well aware of the potential feedback e¤ects and, therefore, potential en-
dogeneity bias in our main coe¢ cients. To address these concerns, we collect
a series of instruments documented in the political science literature and use
them in a panel instrumental variable (IV) approach. While a good …rst step to
examining the e¤ects of Fund programs on capital ‡ows, our LSDV and panel IV
estimations are unable to capture the potentially rich dynamics between these

7 See Janus and Riera-Crichton (2015).

5

variables. To understand these dynamics better, we use the Local Projections
methodology introduced by Jorda (2005) and Stock and Watson (2006) to esti-
mate a set of e¢ cient impulse response functions. As a …nal robustness check,
we combine our IV analysis with the local projections methodology.

Stylized Facts on IMF Lending and Gross Capital Flows

Table 1 shows the summary statistics for IMF programs. As mentioned above
we have a large number of programs (147 program onsets) but, perhaps even
more remarkable, approximately 23 percent of our observations correspond to
an ongoing IMF program. Countries in our sample underwent, on average, three
IMF programs during our sample period. There is a large variation in the size of
the programs both in absolute terms and relative to the countries quota with an
average of 1.3 billion SDRs or 121 percent of the country’s quota at the Fund.
To complete the dynamic view of our Fund Program data, Table 2 presents a
transition probability matrix. This matrix shows that those countries without
an ongoing program have around a 3 percent chance of an onset, while those
undertaking a program face around a 90 percent probability of continuation.

In the last part of the paper, we study the interaction between IMF assis-
tance programs and di¤erent crisis types. While, by design, an IMF presence
is closely related to balance of payment problems, many of the episodes stud-
ied are not associated with the standard crisis indicators (currency, banking or
debt crises). This may be because the country avoided a deeper deterioration of
its macroeconomic situation or because the Fund was present in a (successful)
preventive role. In those cases in which the countries do descend into macro-
economic turmoil, the reaction of capital ‡ows to a Fund program could di¤er
signi…cantly across the varying types of underlying crisis.
In this paper, we
focus on four types of crisis: currency, banking and sovereign debt from both
a domestic and a foreign perspective. Our data on economic crises is based on
Carmen Reinhart’s variety of crises dataset. As her dataset does not cover all
of our sample countries, we have also used information from S&P, Laeven and
Valencia (2013) and Broner et al. (2013). Table 3 gives us an idea of the number
and distribution of assistance program onsets across our sample as well as the
interaction between program onsets and economic turmoil. From this table we
observe that around 56 percent of Fund program onsets are in crisis-hit coun-
tries, with the programs begun in the midst of a currency, banking, external
sovereign debt and domestic sovereign debt crisis in 61, 50, 43 and 18 percent
of the times, respectively.8 The table also provides information on the average
number of IMF programs per country and the total number of countries with at
least one onset. We have 39 countries with at least one program onset, among
these, each country has an average of 3.7 onsets during the sample period.

Table 4 shows the summary statistics for the di¤erent measures of Capital
Flows and explanatory variables used in the paper. We observe that Total Gross

8 Nothing precludes an IMF program from being put in place against the backdrop of a

twin or triple crisis.

6

Flows (sum of the four components of equation 1) hover at around 28 percent
of GDP. Most of these ‡ows originate from the private sector (23 percent). If
we split the sample purely by the direction of the ‡ow, we observe that, in our
sample, total private ‡ows are divided into 12 percent in‡ows and 10 percent
out‡ows. If we split the ‡ows by residence of origination, we discover that the
volume of CIF revolves at around 7 percent of GDP and COD at around 4.5
percent. Splitting the sample by type of ‡ow, we observe that most of the
recorded ‡ows correspond to the category of “Other Investment” (14 percent
of GDP), which is dominated by international banking transactions. Foreign
Direct Investment (FDI) with 5.5 percent and Portfolio Investment with 3.6
percent follow. When looking at the four components from our net in‡ows de-
composition, we observe that the typical ‡ows -in‡ows from increases in foreign
liabilities and out‡ows from increases in foreign assets- are the largest com-
ponents with 10 and 7.5 percent of GDP, respectively.
Investment reversals
(out‡ows from decreases in foreign liabilities) and domestic retrenchment (in-
‡ows from decreases in foreign assets) follow with around 3 percent of GDP
each.

Figure 1 shows the evolution of gross ‡ows against the backdrop of the num-
ber of programs in e¤ect. Decomposing Total Gross ‡ows into Private and
Public Gross Flows, we identify two distinct waves of …nancial integration. The
…rst wave runs from the end of the EMS crisis in 1993 to the beginning of
the Asian Crises in 1997. After the Asian and Russian Crises (1997-98), pri-
vate gross ‡ows slowly declined until the end of the Argentinean crisis in 2002.
The second wave arrives in the latter part of the so-called “Great Moderation”
(2004-08). During this period, total private gross ‡ows in our sample averaged
30 percent of GDP. At the same time, the number of ongoing programs declined
from an average of around 12 to 2. The series of O¢ cial Gross Flows shows an
interesting break in its volatility around the time of the Asian crises. Before the
crises, high levels of volatility, reaching peaks above 10 percent of GDP, char-
acterized the o¢ cial ‡ows. After 1997, the series remained subdued at around
3 percent of GDP. Another interesting decomposition of Gross Flows, shown in
…gure 1, focuses on gross ‡ows by residence and by direction of ‡ow. Looking at
the in‡ow/out‡ow decomposition by residence we observe the collapse of both
measures, but especially in‡ows, during episodes of external turmoil. This, of
course, follows on the footsteps of the sudden stops literature. On the other
hand, looking just at the direction of the ‡ow, we observe large surges in out-
‡ows and, to a lesser degree, in‡ows driven by foreign investment reversals and
domestic retrenchment during crises.

Least Squares Dummy Variables Estimation

In our basic speci…cation, we use the following simple LSDV estimation:

Yit = (cid:11)i + (cid:14)t +

4

n=1
X

(cid:18)n IM Fit

n + (cid:12)Xit

1 + (cid:22)it;

(cid:0)

(cid:0)

7

where Yit represents the di¤erent components of the net in‡ow of country it
at time t, IM Fit is a dummy indicator signaling whether country i signed an
IMF program at time t, and Xit is a vector of controls that include four lags of
GDP growth, the HY Index, the Fed Funds rate, the Chinn-Ito Index of capital
openness, the VIX index, and a crisis indicator taking value one whenever the
country is in a crisis. Our regressions also include country and time …xed-e¤ects
and a time trend.

Results from this simple speci…cation are displayed in Table 5.

In terms
of the coe¢ cients of the controls, there are no surprises. As in Alberola et
al.
(forthcoming), economic growth helps increase foreign in‡ows but is not
so clearly related to out‡ows. Good High-Yield performance promotes both
in‡ows and out‡ows, increasing total gross ‡ows.
Instead, …nancial turmoil
slows foreign in‡ows signi…cantly. Capital openness seems to encourage total
gross ‡ows and …nancial volatility (VIX) deters domestic out‡ows.

Regarding the e¤ect of IMF programs, to help our analysis, we report the
sum of all lagged coe¢ cients of the IMF onset dummy. We measure signi…cance
with a Wald Test of the null of the sum of the four coe¢ cients being zero.
From the table, we can observe that, related to the previous literature, program
onsets seem to have no catalytic e¤ect on net in‡ows, pushing countries into
larger current account surplus and having no e¤ect on total private gross ‡ows.
A more complete story surfaces when looking at the decomposition of the net
in‡ow by direction of ‡ows and by origination of those ‡ows. The last two
columns of Table 5 show the reaction to the onset of a Fund program by foreign
in‡ows and resident out‡ows. From these two columns it becomes clear that
the “no e¤ect” on total private ‡ows results from a weighted average of two
important and opposite forces. On the one hand, the program onset seems to
have an “anti-catalytic”e¤ect on in‡ows from foreigners, prompting a relatively
large decrease of those in‡ows in the year following the onset. On the other, it
seems to exercise a catalytic e¤ect of similar size on out‡ows from residents. In
other words, the Fund program onset prevents to some degree domestic capital
‡ight. The size and timing of these e¤ects, however, di¤er. While after four
quarters the accumulated decrease in in‡ows from foreigners and out‡ows from
residents are similar at around 50% of GDP, the bulk of the in‡ow e¤ect occurs
in the …rst quarter following the program’s establishment and the bulk of the
out‡ow e¤ect with a lag of two quarters. We explore these interesting dynamics
in the sections below.

Panel Instrumental Variables Estimation

Eichengreen and Mody (2003) argue that, when trying to understand the e¤ect
of IMF programs on macroeconomic outcomes, it is necessary to control for the
fact that selection into such programs is non-random, as this could bias the esti-
mated coe¢ cients. In this section, we apply an instrumental variables approach
to tackle this problem. As described below, our choice of instruments is guided
by a signi…cant body of research that has focused on understanding the politi-

8

cal and geo-strategic determinants of IMF lending. As noted by Edwards and
Santaella (1993), Barro and Lee (2005) and Saravia (2013), the literature has
uncovered a set of geo-political and institutional determinants of IMF lending,
which can be used to address endogeneity concerns. More speci…cally, we base
our identi…cation on four di¤erent sets of political factors: internal IMF politics,
borrowing country’s politics, geo-politics and o¢ cial sector politics.

As regards the role of internal IMF politics, Barro and Lee (2005) and Saravia
(2011) argue that a country’s quota at the IMF is also a signi…cant determinant
of IMF …nancial support.9 Country quotas can serve as an instrument to the
extent that they indicate the country’s political power within the institution.
In turn, we model the borrowing country’s political factors as follows. Vreeland
(2006) observes that countries where the political system has more veto power
are more likely to have IMF programs and that countries are less likely to sign
IMF programs when elections are coming up soon. Relatedly, Dreher (2002)
shows that IMF programs are more likely to go o¤-track ahead of elections.
In turn, Edwards and Santaella (1993) …nd that dictatorial regimes are less
likely to engage with the IMF. They rationalize such results as follows. An
important role of international organizations is to do national governments’
“dirty work.”By involving multinational bodies in the decision-making process,
local politicians can shield themselves from the political fallout associated with
unpopular policies. This implies that governments with a more unstable political
base, and thus likely to su¤er more severely from unpopular policies, will recur
more frequently to the IMF. A second implication of this public choice view is
that, other things being equal, countries with dictatorial regimes will have a
smaller incentive to request IMF assistance. This is because dictatorial regimes,
in general, can withstand unpopular adjustment programs without su¤ering
serious political consequences.
In turn, Tacker (2000), Barro and Lee (2005)
and Dreher and Sturm (2006) provide us with geo-political instruments. They
argue that political proximity, as measured by the various countries’ voting
alignment with the US (and other advanced economies) at the United Nations
and other international fora, helps explain IMF lending.10 Finally, we use two
variables associated with the politics of the o¢ cial sector. First, we use the
signing of an agreement with the Paris Club, which automatically requires the
signing in turn of an IMF program. Papi et al.’s (2014) analysis of the e¤ect of
IMF lending on banking crises successfully uses ‡ows of development assistance
(ODA ‡ows) into the economy as an instrument for IMF lending. We follow
them and include that variable in our estimations.

With this identi…cation strategy in mind, we estimate the following model

of the e¤ects of IMF programs on gross capital ‡ows:

9 Relatedly, Dreher and Vaubel (2004) and Copelovich (2004) include the total amount of
resources available to the Fund as a determinant of IMF lending. Also overall resources and
fresh injections of resources can be used as instruments as they may re‡ect IMF-bureaucrats
incentives to lend (Dreher and Sturm, 2006).

1 0 http://www.uni-heidelberg.de/fakultaeten/wiso/awi/professuren/intwipol/datasets.html.

9

IM Fit = (cid:11)i + (cid:14)t + 'Xit

1 + (cid:13)Zit

1 + "it;

(cid:0)

(cid:0)

Yit = (cid:11)i + (cid:14)t +

(cid:18)n

4

n=1
X

IM F it

n + (cid:12)Xit

1 + (cid:22)it:

(cid:0)

(cid:0)

(2)

(3)

d
Equation (2) models the presence of the International Monetary Fund. Equa-
tion (3) models the determinants of gross ‡ows. Equation (2) is estimated using
a lineal panel data model. Yit represents the di¤erent types of capital ‡ows
used in the analysis. Xit
1 covers a set of lagged controls including, as before,
output growth, foreign interest rates, capital control measures, crisis dummies
and foreign …nancial volatility measures. In turn, Zit
1 contains the political
and geo-strategic factors used to instrument the IMF’s presence. The variable
IM F it
n de…nes the estimated likelihood of signing a program with the Fund
obtained from equation (2). Our regressions include country …xed e¤ects, time
e¤ects and a time trend in an e¤ort to capture the increases in global …nancial
d
integration. Finally, we use HEC errors clustered by country.

(cid:0)

(cid:0)

(cid:0)

(cid:0)

We include in Zit

1 all of the instruments discussed above. The …rst-step
results are presented in Table 6a. Most of our instruments are highly signi…-
cant. Even more importantly, they display the expected signs. Thus, countries
with more in‡uence (via IMF quota, their presence on the UN Security Council
or their alignment with the US at UN voting) are more likely to be granted
assistance. Also as expected, dictatorial regimes are less likely to ask for sup-
port than democracies. And, again as expected, countries are less likely to
request assistance during and prior to elections. Finally, higher ODA assistance
and negotiations with the Paris Club function as robust predictors of countries
accessing IMF resources.

In the second step, we regress our various gross capital ‡ows measures against
the instrumented lag of the IMF indicator and our set of exogenous determinants
of the gross ‡ows used in the previous speci…cation.11 Table 6b shows the results
for the gross ‡ows equation (second step). As before, we provide the sum of the
coe¢ cients of the four lags on every IMF lending agreement. Our IV analysis
corroborates the main set of results found in the LSDV speci…cation. First,
when we focus on the direction of the ‡ows, we …nd that IMF programs are
accompanied by a greater reduction in the volume of private capital ‡owing in
(be it of resident or foreign origin). In this dimension, one could argue that the
IMF is not capable of catalyzing the entry of capital into the intervened economy.
There is, however, also no evidence of signi…cantly higher capital out‡ows. This
means that, at least, the signing of an IMF program does not accelerate the exit
of capital already invested in the economy. Second, from a residence perspective
(last two columns), we again obtain very stark and interesting results. While the
IMF is able to catalyze domestic capital ‡ows (reducing the domestic capital
drain), it does not seem to be able to reduce the capital ‡ight by foreigners.

1 1 Since the results from our exogenous controls are very similar those found in Table 5, we

decided not to include them. A complete set of results can be obtained by request.

10

In fact, if anything, IMF programs apparently trigger further foreign capital
‡ight. It is important to note that when using our IV estimation, we step out
of the original di¤-in-di¤ framework making it di¢ cult to relate the estimated
coe¢ cients to the relative size of the impact on capital ‡ows.12

Table 7, which presents our estimates of domestic and foreign ‡ows broken
down by those entering and those exiting the economy, further reinforces the
idea that the IMF signi…cantly a¤ects the behavior of domestic investors. The
estimated coe¢ cients show that resident investors are more likely to repatriate
their foreign assets, compensating somehow for the reduced entry of fresh foreign
capital. Finally, Table 8 repeats the analysis but this time using the categoriza-
tion of the capital ‡ows. Thus, we distinguish ‡ows of entry and exit for FDI,
portfolio investment and other investment. Although we also …nd a signi…cant
negative e¤ect on the in‡ow of foreign FDI, the results show that a program’s
e¤ects are stronger on other investment categories. As this indicates that the
results obtained when looking at broader gross ‡ows measures are largely driven
by domestic banks’‡ows, we conclude that IMF catalysis appears to work best
vis-à-vis domestic banks.

Dynamic e¤ects

So far, we have not focused on the rich dynamic responses of capital ‡ows to the
inception of an assistance program. In this section, we study such dynamics by
presenting a set of representative cumulative impulse response functions using
the local projections methodology.

In our cumulative impulse response function estimation strategy, we follow
Jorda (2005), Stock and Watson (2007), and others in the use of linear “local
projections”(LP) for the construction of our impulse response functions (IRFs).
This methodology allows us to directly project the behavioral reaction of gross
private capital ‡ows to the signing of an IMF …nancial assistance program by
computing estimates of the h-step ahead cumulative average treatment e¤ect
on the gross ‡ows variables.13 This methodology provides a ‡exible alternative
to VAR approaches. As described by Jorda (2005), linear projections can be
estimated by simple single regression techniques (LSDV in our case) and they
are more robust to misspeci…cation errors. While widely used in the literature,
as explained in Ramey (2014), this method does not consistently dominate the
standard Structural VAR method for calculating impulse responses of endoge-
nous variables with contemporaneous e¤ects. Since local projections do not
impose any restrictions linking the impulse responses at h and h+1, estimates
can display erratic behavior due to the loss of e¢ ciency. Additionally, as the
horizon increases, one loses observations from the end of the sample. Finally, the
impulse responses sometimes display oscillations at longer horizons. Comparing
Jorda to a standard SVAR and a dynamic simulation, Ramey (2012) …nds that

1 2 Our estimated IMF program variable is no longer a 0-1 dummy.
1 3 Local projections are, in practice, regression-adjusted di¤erence-in-di¤erence estimates

that collapse the time series information in a pre and a post period for each step ahead.

11

the results are qualitatively similar for the …rst 16 quarters. For longer horizons,
however, the Jorda method tends to produce statistically signi…cant oscillations
not observed in the other two methods. Since, in this study, we are interested
in the short- and medium-horizon e¤ects of fund programs on Gross Flows we
can safely disregard these drawbacks.

In our basic linear speci…cation, each response of changes in capital ‡ows to
contemporaneous onset of …nancial assistance programs at horizon h is obtained
from the following equation:

Yi;t+h = (cid:11)i;h + (cid:12)hIM Fi;t + (cid:9)h(L)IM Fi;t

4

1 + (cid:8)h(L)
(cid:0)
+ (cid:7)h

4
Xi;t

1+

Yi;t
1 + (cid:27)t;h + (cid:22)i;t;h

(cid:0)

(cid:0)

(cid:0)

(cid:21)

(cid:0)

4

Yi;t+h

0, and

1 for h

Yi;t+h = Yi;t+h

4
Yi;t+h represents the ac-
where
cumulated capital ‡ow measure over GDP at time t + h. The lag polynomials
(cid:9)h(L) and (cid:8)h(L) represent four lags. IM Fi;t represents the dummy for the
signing of an IMF program and Xi;t
1 covers the same set of lagged controls
used in the previous sections. Finally, we include a full set of country and year
dummies. Every equation, for each h, is estimated using a standard LSDV
approach. We use robust Driscoll and Kraay (1998) standard errors to correct
for potential heteroskedasticity, autocorrelation in the lags and error correlation
across panels.

4

(cid:0)

Figures 2 to 8 give us the projected reaction of di¤erent ‡ows to the onset
of an IMF program. Looking at …gure 2, we observe a steady increase in total
gross ‡ows over time after the onset of a program. This increase is driven
entirely by o¢ cial gross ‡ows since the aggregated private ‡ows stay ‡at and
even start decreasing after a year (although the coe¢ cient remains statistically
insigni…cant.) As before, interesting results appear after decomposing the gross
private ‡ows into in‡ows and out‡ows. As we can see from …gure 3, the …rst
asymmetry between foreign in‡ow (CIF) and domestic out‡ow (COD) responses
is represented by CIF’s faster reaction to the program’s onset; we observe a
signi…cant drop in CIF on impact, around 6 percent of GDP. Meanwhile, COD
only starts to react 2 quarters after the program signing, with a similar size
drop. A second asymmetry is observed on the size and standard deviation. CIF
peaks close to an accumulated drop of 10 percent of GDP but it displays larger
standard errors. COD peaks at an accumulated drop of around 15 percent of
GDP with tighter errors. This lends credence to our previous narrative in a
dynamic setting: we observe that the presence of the fund has a signi…cant
catalyzing e¤ect on domestic out‡ows in the medium run while it also seems to
signi…cantly discourage foreign in‡ows only in the very short run. Looking at
the decomposition of private ‡ows by their direction, regardless of the residence
of the originators, gives us a more muted version of the same story. Figure
4 shows how in‡ows react strongly and immediately while out‡ows seem to
decrease mildly only after six quarters. The fact that we found relatively less
robust results when looking at ‡ows by direction is another indication that

12

the presence of the Fund a¤ects the decisions of domestic and foreign agents
di¤erently. This is shown in more detail in …gure 5, where we decompose the
Net In‡ow into four components by direction and origin of ‡ow. Looking at the
four panels of …gure 5, we see that while the presence of the fund deters new
in‡ows from foreigners in the short run and, of less statistical signi…cance, in the
medium run (panel A), it does not lead to capital ‡ight (panel D). If anything,
capital withdrawals from foreigners seem to be reduced in the medium run
(albeit not in a statistically signi…cant way). On the other hand, the presence
of the fund not only seems to help prevent domestic residents from sending their
capital abroad (panel C), it also seems to lead to domestic retrenchment in the
medium run (panel B).

Figures 6 to 8 look at the IRFs for each type of ‡ow. Interestingly, the pat-
tern described for aggregate ‡ows seems to be driven by the “other investment”
component. With cross-country bank loans representing the bulk of this type of
‡ows, we seem to be looking at a “banking story”. Figure 6 shows the reaction
of FDI, CIF and COD to the onset of an IMF program. Given the long-term
nature of these ‡ows, it is not surprising that the point estimates of the IRFs
remain relatively stable after the shock. In any case, we do observe an increase
in out‡ows in the medium run peaking at around 2 percent of GDP after 5
quarters and a statistically signi…cant accumulated decrease of FDI in‡ows at
around 3 percent of GDP after 2 quarters.

Figure 7 turns to the reaction of Portfolio (debt and equity) ‡ows. Not sur-
prisingly given the short-term nature of these ‡ows, we observe larger variations
among the point estimates of these IRFs. Interestingly, the IMF seems to be
successful at reducing signi…cantly the amount of COD in the …rst 4 quarters
with an accumulated reduction peaking close to 4 percent of GDP. Again, the
IMF presence seemed ine¤ective in promoting portfolio CIF, although we do
observe an increase in these ‡ows after 2 years. Finally, …gure 8 shows the
reaction of “Other Investment Flows”(OI). “Other Investment”‡ows are com-
posed of international loans, trade credits, currency and other ‡ows. The bulk
of these ‡ows lay accumulated in the international loans category. Figure 8
shows that the reaction of both OI CIF and OI COD is an order of magnitude
larger than what we observed with FDI and PI. In this case, the pattern of both
IRFs mimics the general pattern described in the beginning of this section. CIF
decrease around 5 percent of GDP on impact and peak after …ve quarters at
around 12 percent of GDP. COD take 2 quarters to react but then, after just
six quarters, peak at an accumulated decrease of around 16 percent of GDP.
While the reduction of both CIF and COD is statistically signi…cant at 95%
con…dence level, errors are smaller for COD.

Next, as a robustness check given the potential endogeneity issue described
in the previous section, we follow Jorda et al. (2014) and use an instrumental
variables approach in our local projection regressions:

13

4

Yi;t+h = (cid:11)i;h + (cid:12)hIM Fi;t + (cid:9)h(L)
+ (cid:8)h(L)

IM F i;t
Yi;t
d
IM F i;t represents the signing of an IMF program, instrumented using

1 + (cid:27)t;h + (cid:22)i;t;h

1+
(cid:0)
1 + (cid:7)h

Xi;t

4

4

(cid:0)

(cid:0)

where
the variables introduced in the previous section.

d

Figures 9 and 10 show the results of these Instrumented Local Projections.
In …gure 9, we report the IRFs for foreign in‡ows (CIF) and resident out‡ows
(COD) to a one standard deviation increase in the estimated probability of a
Fund program onset. The results are even stronger than those using the program
onset dummy directly. The same can be said when we decompose the capital
‡ows by direction in …gure 10.14

As mentioned before, using an IV methodology pushes us away from the orig-
inal di¤-in-di¤ framework, making it di¢ cult to relate the estimated coe¢ cients
with the relative size of the response. Nevertheless, in this con…guration, our
instrumented IMF variable proxies for the public expectations on IMF action.
Thus, we read …gures 9 and 10 as the capital ‡ows reaction to rising expectations
of a Fund program onset. In fact, given that expectations seem an important
driver of capital ‡ows, to make sure that our previous results are not biased
by reactions to expectations, we upgrade our previous local projection speci…-
cation including a lagged term of our instrumented measure of IMF program
onset. Comfortingly, the results, displayed in Figure 11, show no signi…cant
changes in our original results.

Dynamic e¤ects and underlying vulnerabilities

As mentioned above, an IMF presence could lead to di¤erent e¤ects depending
on the underlying macroeconomic circumstances of the country requesting the
program. In this section we study this possibility. In …gures 12 to 18 we capture
these di¤erences showing the IRFs of the e¤ects of programs under a set of
di¤erent economic crises and we compare them to a baseline IRF under no crisis.
To calculate these non-linear e¤ects we upgrade our original local projection
estimation to include an interaction term with each type of crisis. Thus, our
new estimation strategy is based on the following equation:

4

Yi;t+h = (cid:11)i;h + (cid:12)1;hDi;t + (cid:12)2;h(IM Fi;t
(cid:1)
Yi;t

+ (cid:8)h(L)

Di;t) + (cid:9)h(L)IM Fi;t
1 + (cid:7)h

1+
1 + (cid:27)t;h + (cid:22)i;t;h

Xi;t

(cid:0)

4
where Di;t is a crisis dummy and IM Fi;t represents our IMF dummy. We test
the e¤ects of program onsets in four types of economic turmoil: currency crises,

4

(cid:0)

(cid:0)

1 4 We performed a battery of (unreported) tests, including the Kleibergen-Paap under iden-
ti…cation and weak identi…cation tests and the Hansen’s over identi…cation test, to assess the
validity of our instruments. All of them supported our identi…cation strategy.

14

banking crises and domestic and external sovereign debt crises, and also using
a dummy capturing any type of crisis. We build the baseline IRF from the
coe¢ cients (cid:12)1;h (where we assume IM Fi;t = 0) and we compare these results to
the sum of (cid:12)1;h + (cid:12)2;h (equivalent to assuming IM Fi;t = 1).15 Finally, we test
the statistical signi…cance of the di¤erences between the e¤ects under crisis and
without crisis. This test is equivalent to test for (cid:12)2;h = 0. We include a yellow
marker in the y-axis if the di¤erence is signi…cant at a 90% con…dence level.

Figures 12 to 16 focus on the response of COD. Figure 12 shows the reaction
of COD to a program onset under the presence of any type of crisis. As in the
baseline speci…cation, the e¤ects on out‡ows of a program onset during a crisis
do not appear until the second quarter after the shock.
Interestingly, while
the …nal accumulated response is similar during and outside crisis episodes,
the medium-run response is not. During crisis, the reduction in COD is much
sharper after only 2 quarters, dropping beyond an accumulated 10 percent of
GDP by the third quarter and peaking at 25 percent in the …fth quarter.

When we break down the analyses by type of crises, we observe a range of
interesting asymmetries. As shown in …gure 13, the e¤ects of program onsets
on COD during currency crises are very similar to those of …gure 12. The
IRF during these periods lead to an accumulated drop in COD of around 22
percent of GDP after …ve quarters. During banking crises (…gure 14), we observe
statistically lower point estimates than the baseline only for the second and
third quarter with a maximum drop in COF close to 40 percent after 5 quarters.
Responses during domestic sovereign defaults, collected in …gure 15, show a very
di¤erent story. During these types of events, fund intervention seems to lead
towards a consistently larger catalyzing e¤ect on COD compare to the baseline
model. Our estimates show an ever-decreasing point estimates for COD that
reaches a whopping 100 percent of GDP after 7 quarters. Finally, the e¤ects on
COD of IMF programs during sovereign external crises seem signi…cantly larger
than the baseline model in the medium run (from 2 to 6 quarters out) but
collapse to similar point estimates afterwards. As shown in …gure 16, the COD
responses during these episodes peak at an accumulated drop of 38 percent of
GDP after 3 quarters.

The IRF of CIFs draw a very interesting set of results as well. While, in
general, the presence of the fund lowers CIFs during crises beyond the estimates
found in the baseline (under crises) estimation, in contrast to COD, these dif-
ferences show large and signi…cant e¤ects during currency and banking crisis
and smaller or insigni…cant di¤erences during domestic and external sovereign
crises. To display these e¤ects we turn to …gures 17 to 21.

In …gure 17, we observe that the response of CIF to program onsets during
any type of crisis is always larger than the baseline estimates. Once the program
is in place, CIF drops by 10 percent of GDP on impact and the response peaks
after 8 quarters at around 58 percent of GDP. In contrast, the baseline estimates
are very close to zero in the short run and peak around 40 percent of GDP in

1 5 In other words, we analyze the e¤ect of IMF lending under the baseline of turmoil.

15

the long run. In any case, the di¤erences between these two responses are only
statistically signi…cant at a 90% con…dence level during the …rst 3 quarters.
When we turn to currency crises in …gure 18, the di¤erences in CIF responses
to program onsets against the baseline estimates are very similar to the ones
described for any crisis. During these crises, CIF drop beyond 20 percent of
GDP immediately after the program is put in place and the e¤ect peaks at
84 percent of GDP after 8 quarters. As shown in …gure 19, the CIF responses
during banking crises are signi…cantly lower than those of the baseline estimation
during the …rst 5 quarters after the program is put in place. In this case, the
drop in CIF peaks at 60 percent after 5 quarters. Finally, …gures 20 and 21
show that, as in the baseline, the presence of the fund during sovereign crises
(domestic or external) does not lead to statistically signi…cant moves in CIF.

Implications for the literature

The …ndings in this paper have implications for the theoretical work that studies
the macroeconomic e¤ects of IMF lending. Remarkably, the theoretical litera-
ture in this …eld has no yet modelled the response of resident investors to the
signing of an IMF program by their domestic authorities.
Instead, to date,
work in this area has focused on understanding the e¤ect of IMF lending on the
dynamics of foreign capital ‡ows.16

Given the need to rationalize diverging responses by residents and foreigners,
we posit that models would need to incorporate asymmetries (or frictions). In
their absence, we see no reason for o¢ cial lending to a¤ect foreign and domestic
agents in di¤erent ways. For example, our …ndings are consistent with IMF
lending generating (or operating under the presence of) asymmetric informa-
tion. This could happen because, as in Dvorak (2003) or Evans and Hnatkovska
(2005), domestic and foreign investors may have di¤erent information sets that
the IMF can in‡uence.Alternatively, residents and foreigners might react di¤er-
ently to new "information" from the IMF.17

Relatedly, our results can be thought of as the result of the existence of
pay-o¤ complementarities between the IMF and domestic investors. Under this
mechanism, the key IMF contribution would not be information, but the funding
it provides. We note that such an argument has already been used in the
theoretical literature, but with a focus on foreign investors.18 To the extent that
IMF loans enhance the authorities’capacity to handle the crisis, they also a¤ect
the investors’pay-o¤ from holding domestic assets. Candidate explanations for
this complementarity are numerous. Our preferred one is that, as argued by

1 6 See Morris and Shin (2006), De Resende (2007) and references therein.
1 7 In modelling gross capital ‡ows (although not the IMF), Dvorak (2003) emphasizes the
role of information asymmetries both between and within countries. Similarly, Brennan and
Cao (1997) and Tille and van Wincoop (2008) argue that a domestic retrenchment can occur
if foreign investors are less informed than resident ones about the return of domestic assets
and crises increase this asymmetry.

1 8 See Corsetti et al. (2007) or Morris and Shin (2006).

16

Reinhart and Sbrancia (2011), resident investors are more willing to repatriate
assets when they are con…dent about the strength of their currency or about
the ability of the monetary authorities to manage …nancial instability. This
could happen if the Central Bank is seen to be building an adequate volume of
international liquidity when using an IMF loan.19

Finally, another source of asymmetry between domestic and foreign investors
could be sovereign risk or, more generally, shocks associated with a relative
deterioration of foreign investors’ property rights. For example, Broner et al.
(2008, 2010) show that if sovereign default more likely to be detrimental to
foreigners than to residents then, during crises, the former have an incentive to
sell domestics assets to the latter, potentially leading to gross ‡ows dynamics
like the ones we document and discuss in this paper.

Conclusions

In this paper, we have studied the catalytic e¤ect of IMF lending from a gross
‡ows perspective. Our results show the existence of an asymmetric reaction of
resident and foreign investors to IMF programs. While the IMF does not appear
able to catalyze foreign capital, there is substantial evidence that it does a¤ect
the behavior of resident investors. Remarkably, the change comes from both a
more muted domestic capital ‡ight and an increased repatriation of residents’
savings abroad. Moreover, the relevance of banking ‡ows for understanding
such dynamics makes us posit that IMF catalysis is a banking story.

References

[1] Barro, R. and J.W. Lee (2005), "IMF programs: Who is chosen and what

are the e¤ects?", Journal of Monetary Economics, Vol. 52.

[2] Brennan and Cao (1997), "International portfolio investment ‡ows", Jour-

nal of Finance 52.

[3] Birds G., and D. Rowlands (2002), "Do IMF Programmes Have a Catalytic
E¤ect on Other International Capital Flows?, Oxford Development Studies,
Vol. 30, No. 3.

[4] Broner, F., A. Martin and J. Ventura (2008), "Enforcement Problems and
Secondary Markets", Journal of the European Economic Association: Pa-
pers and Proceedings, 6.

[5] Broner, F., Martin, A., and J. Ventura (2010), "Sovereign Risk and Sec-

ondary Markets", American Economic Review 100 (4).

1 9 Cheng (2015) shows that countries can use foreign reserves to enhance their domestic

economies’resilience to potential risks from balance sheet e¤ects.

17

[6] Broner, F., Didier, T., Erce, A., and S. Schmuckler (2013), “Gross Capital
Flows: Dynamics and Crises”, Journal of Monetary Economics, Vol. 60,
Issue 1.

[7] Broto, C., Diaz-Cassou, J. and A. Erce (2011), "Measuring and explain-
ing the volatility of capital ‡ows towards emerging countries”, Journal of
Banking and Finance - Vol. 35 (8).

[8] Calvo, G. (2003), "Explaining Sudden Stops, Growth Collapse and BOP
Crises: The Case of Distortionary Output Taxes”, NBER Working Papers
9864.

[9] Cheng, G. (2015), "Balance sheet e¤ects, foreign reserves and public poli-

cies", European Stability Mechanism Working Papers No. 3.

[10] Copelovich, M. (2004), "Private Debt Composition and the Political Econ-

omy of IMF Lending", Mimeo.

[11] Corsetti, G., Guimaraes, B. and N. Roubini (2006), International lending of
last resort and moral hazard: A model of IMF’s catalytic …nance, Journal
of Monetary Economics, Vol. 53(3).

[12] Cottarelli, C. and C. Giannini (2002), “Hostages, bed fellows or perfect
strangers: Global Capital Markets and the catalytic e¤ect of IMF Crisis
Lending”, IMF Working Paper No. 02/193.

[13] De Resende, C. (2007), "IMF-Supported Adjustment Programs: Welfare
Implications and the Catalytic E¤ect". Bank of Canada, Working Paper
No. 2007-22.

[14] Diaz-Cassou, J. and A. Garcia-Herrero (2006), "What Kind of Flows Does
the IMF Catalyze and When?". Bank of Spain Working Paper, No. 0617.

[15] Dreher, A. (2003),” The In‡uence of Elections on IMF program Interrup-

tions”, Journal of Development Studies, Vol. 39, issue 6.

[16] Dreher, A., and R. Vaubel (2004), "Do IMF and IBRD cause moral hazard
and political business cycles? Evidence from panel data", Open Economies
Review 15(1). 18

[17] Dreher, A. and J. Sturm (2006), "Do IMF and World Bank In‡uence Voting

in the UN General Assembly?", KOF Working Paper 137.

[18] Dvorak (2013), "Gross capital ‡ows and asymmetric information", Journal

of International Money and Finance 22.

[19] Edwards, M., and J.A. Santaella (1993), “Devaluation Controversies in
the Developing Countries: Lessons from the Bretton Woods Era”, in A
Retrospective on the Bretton Woods System: Lessons for International
Monetary Reform (edited by M.D. Bordo and B. Eichengreen)

18

[20] Edwards, M. (2006), "Signalling credibility? The IMF and catalytic …-
nance", Journal of International Relations and Development, Vol. 9, Num-
ber 1.

[21] Evans, M., and V. Hnatkovska (2014), "International capital ‡ows returns
and world …nancial integration", Journal of International Economics, Vol.
92, Issue 1,

[22] Eichengreen, B. and A. Mody (2003), "Bail Ins, Bailouts, and Borrowing

Costs, Journal of Econometrics, Vol. 36.

[23] Eichengreen, B., Kletzer and A. Mody (2005), "The IMF in a World of

Private Capital Markets", IMF Working Paper 05/84.

[24] Erce, A. (2012), “Does the IMF´s o¢ cial support a¤ect sovereign bond

maturities?”, Bank of Spain Working Paper 1231.

[25] Forbes, K and F. Warnock (2012), “Capital Flow Waves: Surges, Stops,
Flight and Retrenchment”, Journal of International Economics, Vol. 88,
Issue 2.

[26] Ghosh, A., Lane, T., Schultze, G., Bulir, M., Hasmann, J. and A. Mour-
mouras (2002), "IMF Supported Programs in Capital Account Crises",
IMF Occasional Paper No. 210.

[27] Hutchison, M. and I. Noy 2006. Sudden Stops and the Mexican Wave:
Currency Crises, Capital Flow Reversals and Output Loss in Emerging
Markets. Journal of Development Economics 79.

[28] IMF (2013), Sovereign Debt Restructuring – Recent Developments and
Implications for the Fund’s Legal and Policy Framework , International
Monetary Fund, Policy Document.

[29] Janus, T., and D. Riera-Crichton (2015), “International gross capital ‡ows:
a new measure and application to a global panel”, Journal of Policy Mod-
elling, Vol. 35, Issue 1,.

[30] Jeanne, O., Ostry, J. and J. Zettelmeyer (2008), A Theory of International
Crisis Lending and IMF Conditionality, CEPR Discussion Papers 7022.

[31] Jordà, O. (2005), "Estimation and Inference of Impulse Responses by Local

Projections." American Economic Review, 95(1).

[32] Jordà, O., Moritz Schularick and Alan Taylor (2014), “Betting the House”,
Forthcoming in NBER International Seminar on Macroeconomics 2014,
Clarida, Frankel, Giavazzi, and Rey

[33] Laeven, L., and F. Valencia (2013), “Systemic Banking Crises Database”,

IMF Economic Review, 61.

19

[34] Mina, W. and J. Martinez-Vazquez (2002), "IMF Lending, Maturity of
International Debt and Moral Hazard". Georgia State University Working
Paper, No. 03-0119

[35] Mody, A., and D. Saravia (2003) "Catalyzing Private Capital Flows: Do
IMF-Supported Programs Work as Commitment Devices?", IMF Working
Paper 03/100.

[36] Morris, S., and H. Shin (2006), “Catalytic Finance: When Does It Work?”,

Journal of International Economics, Vol. 70.

[37] Papi, L., Presbitero, A., and A. Zazzaro (2015), “IMF Lending and Banking

Crises”, IMF WP 15/19.

[38] Peñalver, A. (2004), "How can the IMF catalyse private capital ‡ows? A

model", Bank of England, Working Paper No. 215.

[39] Reinhart, C., and B. Sbrancia (2011), "The Liquidation of Government

Debt", NBER Working Paper No. 16893

[40] Saravia, D. (2013), "Vulnerability, Crisis and Debt Management: Do IMF
Interventions Increase reliance on short term debt?", International Finance,
Vol. 16, Issue 3.

[41] Thacker, S. (2000), "The high politics of IMF lending". World Politics, 52.

[42] Tille and van Wincoop (2008), "International capital ‡ows", Journal of

International Economics 80.

[43] Vreeland (2006), “IMF program compliance: Aggregate index versus policy
speci…c research strategies”, Review of International Organisations 1..

[44] Zwart, S. (2007), "The Mixed Blessing of IMF Intervention: Signalling

versus Liquidity Support". Journal of Financial Stability, 3(2).

20

APPENDIX 

Graph 1: Gross flows and the IMF in Uruguay (2002) and Turkey (2005) 

Table 1: IMF Program Summary Stats 

Variable 
IMF Ongoing Dummy 
IMF Program Size (SDR Mill) 
IMF Program Size (Rel Quota) 
IMF Program Duration (Months) 

Observations  Mean 
0.231 

4332 

147 
147 
147 

1318.1 
121.7 
20.6 

Std. Dev  Min 

0.421 

3229.9 
223.8 
10.2 

0 

11.6 
15 
5 

Max 
1 

22821.1 
1938.5 
49 

Table 2: Transition probability Matrix for Ongoing Fund Programs 

Origin/End 
0 
1 
Total 

0 

96.79 
10.71 
76.68 

1 
3.21 
89.29 
23.32 

Total 

100 
100 
100 

 
 
 
 
 
 
 
 
 
 
Table 3: IMF programs and economic crises  

Total Onsets  Onsets per Country  Countries with Onsets 

IMF Onset Total 

IMF Onset during All Crisis 

IMF Onset during Currency Crisis 

IMF Onset during Banking Crisis 

IMF Onset during Sovereign Dom. Crisis 

IMF Onset during Sovereign External Crisis 

147 

83 

51 

41 

15 

36 

3.77 

2.59 

1.89 

1.78 

1.67 

2.25 

39 

32 

27 

23 

9 

16 

Table 4: Gross capital flows and control variables. Summary Statistics 

Variable 

Total Gross Flows 

Private Gross Flows over GDP 

Official Gross Flows over GDP 

Inflows from Foreigners (CIF)  

Outflows from Residents (COD)  

Gross Inflows (By Flow direction)  

Gross Outflows (By flow direction) 

FDI flows over GDP 

Portfolio Investment flows over GDP 

Other Investment flows over GDP 

Private Outflows from Liabilities  

Private Inflows from Liabilities 

Private Outflows from Assets 

Private Inflows from Assets 

Federal Reserve Funds Rate 

Real Output Growth (Quarter to Quarter) 

High Yield Index 

Capital Openness Index 

VIX 

Observations  Mean 

Std. Dev.  Min  Max 

2574 

2574 

2574 

2574 

2574 

2574 

2574 

2574 

2574 

2574 

2574 

2574 

2574 

2574 

2574 

2514 

2574 

2574 

2574 

0.28 

0.28 

0.013 

2.795 

0.231 

0.261 

0.047 

0.093 

0 

0 

2.54 

2.661 

0.069 

0.107 

-0.534 

0.954 

0.045 

0.171 

-2.038 

2.537 

0.127 

0.135 

0.104 

0.172 

0.054 

0.085 

0.036 

0.076 

0.139 

0.178 

0.028 

0.048 

0.098 

0.103 

0.075 

0.164 

0.029 

0.078 

0 

0 

0 

0 

0 

0 

0 

0 

0 

2.065 

2.54 

1.787 

1.606 

1.969 

0.626 

1.056 

2.537 

2.064 

4.105 

1.951 

0.14 

8.32 

0.076 

0.121 

-1.633 

0.34 

446.932 

153.475 

131.58 

705.29 

0.506 

0.336 

0 

1 

20.236 

7.297 

11.19 

51.723 

 
 
 
Figure 1 

Table 5:  IMF ONSETS ON CAPITAL FLOWS. LSDV ESTIMATION  

VARIABLES 

CU 

TGF 

PGF 

IMF Onset (t-1) 

0.2890 

-0.0146 

-0.1670 

GROSS 
INFLOWS 
-0.2193 

GROSS 
OUTFLOWS 
-0.0757 

FOREIGNERS 
INFLOWS 
-0.1829 

RESIDENTS 
OUTFLOWS 
-0.0602 

[0.112]** 

[0.098] 

[0.067]** 

[0.078]*** 

[0.089] 

[0.091]** 

[0.086] 

IMF Onset (t-2) 

0.2527 

0.1625 

0.0807 

0.0460 

-0.0064 

-0.0754 

-0.2264 

[0.114]** 

[0.103] 

[0.108] 

[0.108] 

[0.114] 

[0.100] 

[0.100]** 

IMF Onset (t-3) 

0.1301 

0.0761 

0.0071 

-0.0603 

0.0231 

-0.1149 

-0.0890 

[0.105] 

[0.139] 

[0.112] 

[0.113] 

[0.106] 

[0.118] 

[0.105] 

IMF Onset (t-4) 

0.1415 

-0.0231 

-0.0685 

-0.1612 

-0.0561 

-0.1899 

-0.0918 

[0.101] 

[0.099] 

[0.091] 

[0.096] 

[0.101] 

[0.119] 

[0.078] 

Total IMF Effect 

0.791** 

0.195 

-0.144 

-0.388 

-0.114 

-0.565* 

-0.474** 

Real GDP growth (t-1) 

1.2357 

-2.0964 

-2.1245 

-0.3882 

-2.9479 

1.6964 

-0.6772 

[0.617]* 

[0.470]*** 

[0.475]*** 

[0.482] 

[0.511]*** 

[0.396]*** 

[0.424] 

Real GDP growth (t-2) 

-1.3664 

1.0426 

1.4787 

0.8836 

1.6154 

-0.2704 

0.3204 

[0.489]*** 

[0.766] 

[0.590]** 

[0.399]** 

[0.642]** 

[0.442] 

[0.671] 

Real GDP growth (t-3) 

0.5208 

0.4880 

0.1808 

-0.5235 

0.3090 

-0.8050 

0.1248 

[0.452] 

[0.865] 

[0.733] 

[0.499] 

[0.715] 

[0.440]* 

[0.577] 

Real GDP growth (t-4) 

-1.2946 

-0.6776 

-0.2880 

0.9539 

-1.0394 

1.4859 

-0.4484 

[0.421]*** 

[0.559] 

[0.583] 

[0.506]* 

[0.492]** 

[0.386]*** 

[0.406] 

High Yield Index 

0.0030 

0.0036 

0.0032 

0.0017 

0.0018 

0.0052 

0.0049 

[0.002] 

[0.001]*** 

[0.001]** 

[0.002] 

[0.002] 

[0.002]*** 

[0.002]*** 

FED Funds Rate (t-1) 

-0.0032 

-0.0679 

-0.0003 

0.0320 

-0.0057 

0.0172 

0.0169 

[0.069] 

[0.053] 

[0.055] 

[0.067] 

[0.042] 

[0.072] 

[0.046] 

Any crisis dummy (t-1) 

0.2260 

0.2231 

0.0975 

-0.0775 

0.2496 

-0.1828 

-0.0169 

[0.120]* 

[0.083]*** 

[0.103] 

[0.119] 

[0.089]*** 

[0.093]* 

[0.088] 

KA Openness (t-1) 

-0.5314 

0.5338 

0.6152 

0.5408 

0.4237 

0.4956 

0.1825 

[0.375] 

[0.226]** 

[0.266]** 

[0.308]* 

[0.222]* 

[0.306] 

[0.163] 

VIX Index (t-1) 

-0.0074 

0.0088 

0.0211 

0.0143 

0.0131 

-0.0072 

-0.0197 

[0.011] 

[0.008] 

[0.007]*** 

[0.007]* 

[0.011] 

[0.011] 

[0.011]* 

TIME TREND 

YEAR EFFECTS 

YES 

YES 

YES 

YES 

YES 

YES 

YES 

YES 

YES 

YES 

YES 

YES 

YES 

YES 

Observations 

2,374 

2,430 

2,430 

2,430 

2,430 

2,430 

2,430 

Number of Countries 

43 

44 

44 

44 

44 

44 

44 

Adjusted R-squared 

0.138 

0.155 

0.214 

0.185 

0.162 

0.235 

0.102 

Robust standard errors in brackets. *** p<0.01, ** p<0.05, * p<0.1. CU stand for the current account, TGF for total 
gross flows and PGF for private gross flows. Gross inflows and outflows refer to the flows by direction. Finally, 
foreigner inflows is CIF and residents’ outflows is COD. KA Openness refers to the Chin-Ito Index. 

 
 
 
 
 
 
 
 
 
 
 
 
 
Table 6a: IV Estimation (first stage): Determinants of IMF lending 

Table 6b: IV Estimation: Second Stage 

VARIABLES 

CU 

TGF 

PGF 

IMF Onset IV (t-1) 

0.6524 

-0.0587 

-0.4881 

GROSS 
INFLOWS 
-0.5951 

GROSS 
OUTFLOWS 
-0.1680 

FOREIGNERS 
INFLOWS 
-0.4993 

RESIDENTS 
OUTFLOWS 
-0.0714 

[0.261]** 

[0.242] 

[0.174]*** 

[0.183]*** 

[0.225] 

[0.190]** 

IMF Onset IV (t-2) 

0.6020 

-0.0460 

IMF Onset IV (t-3) 

IMF Onset IV (t-4) 

[0.302]* 

[0.208] 

0.3619 

[0.239] 

0.2224 

[0.246] 

-0.0648 

[0.294] 

-0.0170 

[0.264] 

-0.1018 

[0.235] 

-0.1291 

[0.252] 

-0.1883 

-0.2136 

[0.251] 

-0.2747 

[0.262] 

-0.5385 

-0.1144 

[0.254] 

-0.0351 

[0.224] 

0.0387 

-0.4117 

-0.3858 

[0.248] 

-0.6868 

[0.211] 

[0.180]*** 

[0.298] 

[0.260]** 

[0.214] 

-0.5875 

-0.3051 

[0.201] 

-0.1510 

[0.207] 

[0.233]* 

[0.221]** 

Total IMF Effect 

1.839** 

-0.187 

-0.907 

-1.622** 

-0.279 

-1.984*** 

-1.115** 

Controls 

Time trend 

Year effects 

Observations 

Number of Countries 

Adjusted R-squared 

YES 

YES 

YES 

2,302 

43 

0.138 

YES 

YES 

YES 

2,354 

44 

0.152 

YES 

YES 

YES 

2,354 

44 

0.213 

YES 

YES 

YES 

2,354 

44 

0.189 

YES 

YES 

YES 

2,354 

44 

0.158 

YES 

YES 

YES 

2,354 

44 

0.247 

YES 

YES 

YES 

2,354 

44 

0.105 

Robust standard errors in brackets. *** p<0.01, ** p<0.05, * p<0.1. CU stand for the current account, TGF for total 
gross flows and PGF for private gross flows. Gross inflows and outflows refer to the flows by direction. Finally, 
foreigner inflows is CIF and residents’ outflows is COD. Controls included are the same as in table 4.  

IMF presenceIMF presenceIMF presenceIMF presenceDictatorship dummy-0.1216-0.1162-0.1071-0.1082[0.025]***[0.026]***[0.026]***[0.026]***Elections dummy-0.0118-0.0121-0.0112-0.0115[0.006]**[0.006]**[0.006]**[0.006]**Presence in UN Security Council0.01850.02050.0199[0.011]*[0.011]*[0.011]*Alignment with the US at UN voting0.50290.53510.539[0.266]*[0.260]**[0.268]**Paris Club deal dummy0.30920.3108[0.053]***[0.053]***ODA provided by the US14,34816,639[0.427]***[0.368]***Quota at the IMF0.0096[0.033]3,8493,7773,7673,76757565656VariablesNumber of countriesStandard errors in brackets. *** p<0.01, ** p<0.05, * p<0.1. All regressions also include also four lags of real GDP growth, the high yield index, Federal funds rate, Chinn-Ito Index and a crisis dummy.Official sector politicsDomestic politicsGeo-politicsIMF internal politicsObservations 
 
 
 
 
 
 
 
 
 
 
 
 
 
Table 7: Disaggregating by direction and origin of flows 

VARIABLES 

LSDV ESTIMATION 

Total IMF Effect after 4 quarters 

P-Value Wald Test 

Observations 

Number of Countries 

Adjusted R-squared 

IV ESTIMATION (SECOND STAGE) 

Total IMF Effect after 4 quarters 

Observations 

Number of Countries 

Adjusted R-squared 

Controls 

Time trend 

Year effects 

Private Inflows 

Private Inflows 

Private Outflows 

Private Outflows 

From Liabilities 

From Assets 

From Assets 

From Liabilities 

-0.168** 

[0.044] 

2430 

44 

0.19 

-2.195*** 

[0.002] 

2354 

44 

0.199 

YES 

YES 

YES 

0.076** 

[0.045] 

2430 

44 

0.085 

1.089** 

[0.026] 

2354 

44 

0.084 

YES 

YES 

YES 

-0.059 

[0.379] 

2430 

44 

0.168 

-0.421 

[0.482] 

2354 

44 

0.166 

YES 

YES 

YES 

-0.006 

[0.882] 

2430 

44 

0.136 

-0.151 

[0.829] 

2354 

44 

0.138 

YES 

YES 

YES 

Robust standard errors in brackets. *** p<0.01, ** p<0.05, * p<0.1. Controls included are the same as in table 4.  

Table 8: Disaggregating by type of flow 

VARIABLES 

FDI INFLOW 

FDI OUTFLOW 

PI INFLOW 

PI OUTFLOW  OI INFLOW  OI OUTFLOW 

LSDV ESTIMATION 

Total IMF Effect after 4 quarters 

-0.03 

Observations 

Number of Countries 

Adjusted R-squared 

[0.459] 

2374 

43 

0.108 

IV ESTIMATION (SECOND STAGE) 

Total IMF Effect after 4 quarters 

-1.404* 

Observations 

Number of Countries 

Adjusted R-squared 

Controls 

Time trend 

Year effects 

[0.075] 

2302 

43 

0.119 

YES 

YES 

YES 

0.018 

[0.167] 

2322 

42 

0.138 

0.605 

[0.28] 

2254 

42 

0.134 

YES 

YES 

YES 

-0.005 

[0.8] 

2386 

43 

0.088 

-0.573 

[0.367] 

2314 

43 

0.09 

YES 

YES 

YES 

-0.025 

[0.475] 

2366 

43 

0.043 

-0.327 

[0.632] 

2294 

43 

0.041 

YES 

YES 

YES 

-0.144* 

[0.076] 

2374 

43 

0.207 

-0.163** 

[0.022] 

2430 

44 

0.044 

-1.793*** 

-1.325** 

[0.009] 

[0.023] 

2302 

43 

0.214 

YES 

YES 

YES 

2354 

44 

0.048 

YES 

YES 

YES 

Robust standard errors in brackets. *** p<0.01, ** p<0.05, * p<0.1. Controls included are the same as in table 4.  

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Figure 2: Total and Private Gross Flows IRFs to IMF Onset 

Figure 3: CIF and COD IRFs to IMF Onset 

 
 
Figure 4: Gross Private Outflows/Inflows IRFs to IMF Onset 

Figure 5: INDIVIDUAL COMPONENTS OF THE NET INFLOW IRFs to IMF Onset 

Panel A   

Panel B 

Panel C  

Panel D 

 
 
 
 
 
 
 
  
 
  
 
 
  
 
 
 
 
 
 
 
 
Figure 6: FDI CIF/COD IRFs to IMF Onset 

Figure 7: Portfolio Investment CIF/COD IRFs to IMF Onset 

 
 
 
Figure 8: Other Investment CIF/COD IRFs to IMF Onset 

Figure 9: Impulse response to 1 Standard deviation shock to the expectation of IMF Onset 

 
 
 
 
Figure 10: Individual components of the net inflow: IRFs to 1 standard deviation shock to the expectation of IMF Program Onset 

Panel A   

Panel B 

Panel C  

Panel D 

 
 
 
 
 
 
 
  
 
  
 
 
  
 
 
 
 
 
 
 
 
Figure 11: CIF and COD IRFs to IMF Onset controlling for Expectations 

Figure 12: Gross outflows IRF to any type of crisis with and without onset of IMF program  

13 

 
 
 
 
 
 
Figure 13: Gross outflows IRF to currency crisis with and without onset of IMF program 

Figure 14: Gross outflows IRF to banking crisis with and without onset of IMF program 

14 

 
 
 
 
 
 
 
Figure 15: Gross outflows IRF to sovereign domestic debt crisis with and without onset of IMF 
program 

Figure 16: Gross outflows IRF to sovereign external debt crisis with and without onset of IMF 
program 

15 

 
 
 
 
 
 
Figure 17: Gross inflows IRF to any type of crisis with and without onset of IMF program 

Figure 18: Gross inflows IRF to currency crisis with and without onset of IMF program 

16 

 
 
 
 
 
 
 
Figure 19: Gross inflows IRF to banking crisis with and without onset of IMF program 

Figure 20: Gross inflows IRF to sovereign domestic debt crisis with and without onset of IMF 
program 

17 

 
 
 
 
 
 
 
Figure 21: Gross inflows IRF to sovereign external debt crisis with and without onset of IMF 
program 

18 

 
 
 
 
 
 
6a Circuit de la Foire Internationale
L-1347 Luxembourg

Tel: +352 260 292 0

www.esm.europa.eu

info@esm.europa.eu

©European Stability Mechanism 12/2015