DP2013-07

Crises, Economic Integration a nd
Growth C ollapses in Afric an Countries *Abdilahi ALI
Katsushi S. IMAI

Revised March 9, 2015

* The Discussion Papers are a series of research papers in their draft form, circulated to encourage
discussion and comment. Citation and use of such a paper should take account of its provisional character.
In some cases, a written consent of the author may be required.

Crises, Economic Integration and Growth Collapses in African Countries

Abdilahi Ali
School of Management & Business, Aberystwyth University, SY23 3AL, UK
Katsushi S. Imai*
School of Social Sciences, University of Manchester, M13 9PL, UK & RIEB, Kobe University,
Japan
First Draft: 1st March 2013
Revised on 9th March 2015

Abstract
The objective of this paper is to explore the effects of crises and openness on a large sampleof African countries. Focusing on sudden stops, currency, twin and sovereign debt crises, thepaper shows that crises are associated with growth collapses in Africa. In contrast, openness
is found to be beneficial to growth. More specifically, consistent with standard
Mundell-Flemming type models, greater openness to trade and financial flows is found to
mitigate the adverse effects of crises. These findings are robust to various measures of both
openness and crises as well as to endogeneity concerns.

Keywords: Financial crises, economic integration, growth, Africa
JEL classification: G01, F15, F43, 055*Corresponding Author
Katsushi Imai (Dr)
Department of Economics, School of Social Sciences
University of Manchester, Arthur Lewis Building,
Oxford Road, Manchester M13 9PL, UK
Phone: +44-(0)161-275-4827
Fax: +44-(0)161-275-4928
E-mail: Katsushi.Imai@manchester.ac.uk

Acknowledgements
The authors acknowledge valuable comments from Bernard Walters, Md Azam, Laurence Roope,
Zeeshan Atiqand, Natina Yaduma, Obbey Elamin, Adams Adama, Maria Quattri and participants inInternational Economic Association 16th World Congress (Beijing, China), the CSAE 25th Conference on
Economic Development in Africa (University of Oxford), and seminars at the University of Manchester.
Only the authors are responsible for any errors.

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Crises, Economic Integration and Growth Collapses in African Countries

1 Introduction
It is largely accepted that trade and financial openness can increase the propagation of businesscycle fluctuations among countries, making them more vulnerable to contagion. However,notwithstanding the potential risks associated with globalisation, an increasing number ofAfrican countries have embarked on policies of trade and financial liberalisation under the
auspices of IMF and World Bank sponsored stabilisation programmes (Zagha and Nankani,2005). As a result, Africa is more integrated into the global economic system today than it was
few decades ago. Yet, like developing countries in other regions, African economies have also
encountered their share of economic and financial crises (Laeven and Valencia, 2008).1 As
recent global events illustrate, crises can have devastating effects on economic activity and can
hit countries with weak, and even sometimes those with strong, macroeconomic fundamentals.
Thus, economists and policy-makers are increasingly concerned with understanding the genesis,
evolution and consequences of economic crises.
The objective of this study is to explore how crises and openness to both trade andfinancial flows affect economic growth in Africa. More specifically, we examine whether greater
openness to trade and financial flows exacerbates or lessens the adverse effects of financial
crises. To our knowledge, ours is the first paper to explore the effects of both crises and openness
on growth performances in the context of African countries, while the existing literature has
focused mainly on emerging markets or has used cross-country data covering a fewer number ofAfrican countries (e.g. Aguiar, 2005; Joyce and Nabar, 2009). As far as we are aware, no paper1

In fact, during 1970 to 2007, the African region has, in absolute numbers, encountered more systemicbanking and currency crises than any other region (Laeven and Valencia, 2008).

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has examined the interactive effect of crises and openness on economic growth. We also
distinguish between four different types of crises, namely, sudden stops, currency, twin andsovereign debt crises.
A ‘sudden stop’ in capital inflows is a type of crisis in which access to foreign capital isabruptly and severely curtailed, precipitating large swings in the capital account of the balance of
payments. It is closely associated with current account reversals (from large deficits to smaller
deficits/ surpluses), reserve depletion, growth collapses as well as currency and sovereign debt
crises (Calvo, 1998)2. A currency crisis, on the other hand, occurs when investors substituteaway from a particular country’s assets in anticipation of a potential depreciation of the currency,
while a sovereign debt crisis involves a default or restructuring of debt obligations. Twin crises,
first coined by Kaminsky and Reinhart (1999), arise when currency crises are followed bybanking crises. As shown by, for instance, Bordo et al. (2001), twins tend to have much more
harmful effects on the economy relative to either currency or banking crises on their own. The
different types of crises may hit simultaneously, as they may be triggered off by common
underlying factors, and one crisis may also help precipitate another.
The results of this study show that financial crises are associated with growth collapses inAfrica. In contrast, economic openness is found to be beneficial to growth. More specifically, we
find that, consistent with standard Mundell-Flemming type models, greater openness to trade andfinancial flows tends to mitigate the adverse effects of crises.
The paper is organised as follows. Section 2 contains a brief overview of the2

Sudden stops and the accompanying liquidity constraints imply that the current account must be abruptly
adjusted (i.e. the deficit should be reduced or reversed). This can be avoided by drawing down or depleting thereserve holdings of the central bank, provided there are enough reserves and the central bank is willing to do so(however, reserve depletion may initiate currency crises) or, alternatively, by seeking emergency funding frominternational financial institutions. In any case, a current account reversal can be very painful, as labour andgoods markets tend to be inflexible in the short-run.

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openness-crisis relationship. Section 3 presents the data and methods used. Section 4 reports anddiscusses the results, while the main findings are placed in a broader context in Section 5.
Section 6 offers concluding remarks.

2 The openness-crisis interaction
One can identify two opposing hypotheses as to whether economic and financial integration
mitigate or exacerbate the adverse effects of financial crises. On the one hand, some have argued
that openness can be an important crisis amplifier, in that it can expose countries to external
shocks, while others suggest that it can act as a crisis buffer insofar as it can help accommodate
external shocks.

Openness as a crisis amplifier
As summarised by Cavallo and Frankel (2008), a number of arguments have been put forward insupport of the view that openness to trade can trigger or exacerbate crises. In particular, countries
that are more integrated into the global economy are more likely to be subject to external shocks
emanating from, for example, trading partners. As a result, these economies are more prone toexport collapses and/or diminishing trade credits which in turn can trigger sudden stops and othertypes of crises. Empirical findings by Milesi-Ferretti and Razin (2000) and Easterly et al. (2001)
suggest that openness to trade is closely linked to output volatility and a higher likelihood ofexternal crises.
With respect to capital account openness, economists such as Stiglitz argue that it canaggravate pre-existing market distortions caused by informational asymmetries, credit market
imperfections, poor institutions and moral hazards, increasing the likelihood of crises (Stiglitz,

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2000). While the overwhelming majority of economists, including Stiglitz, remain in favour oflong-term private capital inflows (e.g. foreign direct investment), many point to the destabilising
effects of volatile and pro-cyclical surges in inflows. Hence, it has been argued that capital
account openness may lead to increased inflows of short term capital and a higher risk of abrupt
reversals (Agenor, 2004). Others assert that capital movements, as a result of financial openness,
may increase macroeconomic instability (e.g. upward pressures on the exchange rates, asset pricebubbles, credit booms, higher inflation, consumption growth volatility) and lead to the presenceof more short-term, high risk speculative capital in the economy (Arestis, 2005).

Openness as a crisis buffer
The idea that openness to trade can lower the probability of crises or, alternatively, lessen theadverse effects of external crises is not new in economics. For instance, a number of studies havepostulated that there is an inverse relationship between trade openness and default probabilities(Schimmelpfennig et al. 2003; Borensztein and Panizza, 2009). More precisely, countries withhigher trade activities are less likely to default on their international obligations since theirtrading partners could impose harsh sanctions on them in the event of a default (Rose, 2005).
An alternative argument suggests that trade openness lessens the adjustment costs
associated with external crises. In particular, it has been suggested that open economies are more
likely to ‘export their way out of a crisis’. This was first noted by Sachs (1985) who observedthat in the early 1980s Latin American countries were subject to numerous debt crises, in spite of
having similar levels of debt to GDP ratios to those of Asian countries, precisely because of theirlower trade openness and hence their inability to generate foreign exchange to service their debt.
Focusing on Latin American and Asian countries, Guidotti et al. (2004) have also shown that5

countries with open trade regimes tend to have better growth performances and quicker
recoveries in the face of sudden stop crises than those with closed economies.
How trade openness reduces the adjustment costs of external shocks has been elaborated
by, among others, Edwards (2004), Cavallo and Frankel (2008), Calvo et al. (2003) and
Ripoll-i-Alcon (2010). Suppose that an economy has to abruptly adjust to a shock (e.g. a sudden
stop episode). In the first instance, assume that expenditure-switching policies are not possible(i.e. the exchange rate is fixed). In this case, the country must implement spending cuts to satisfy
its intertemporal resource constraint and thus run a current account surplus. In the standard
Keynesian and Mundell-Flemming type of models, the severity of the adjustment is negatively
related to the marginal propensity to import, with a higher propensity implying lower adjustment
costs. Thus, more open economies would, ceteris paribus, suffer a less severe contraction.3
Similar conclusions can be reached if one uses traditional tradable/nontradable models.
To illustrate this, assume that it is now possible for the country to implement
expenditure-switching policies. In this case, to improve the trade balance, the relative price of
non-tradables must fall. Hence, the needed adjustment can, at least in part, be achieved through anominal and real depreciation of the exchange rate. This would in turn, following sticky-priceopen economy models and conventional Mundell-Fleming type models, improve the recovery of
the economy through increased competitiveness.4
Recent experiences from emerging markets, however, show that the effect of depreciationon output can in fact be contractionary particularly when there is a currency mismatch brought3

Output losses would be inevitable if wages and prices are rigid. This is more likely to be the case in the
short-run.
4
For a survey, see Lane (2001). The beneficial effects of the depreciation would depend on a number offactors, including whether the Marshall-Lerner condition holds.

6about by the so-called “original sin”.5 As shown by a number of theoretical (see for example,
Aghion et al. 2001) and empirical contributions (see for example, Aguiar, 2005 on Mexico), thebalance sheet effects of a depreciation can cause output contraction as a result of dwindling firm
net worth. However, as emphasised by Cavallo and Frankel (2008), the required devaluation may
not be large for countries with higher trade to GDP ratios and, in turn, the balance sheet effects
need not be large. Consequently, the prediction is that, ceteris paribus, more open countries canmitigate the adverse effects of external shocks better than closed economies, which are more
likely to end up in a recession due to the need implement more severe adjustments.
As for financial integration, Edwards (2004) and references cited therein postulate that
financial openness, as trade integration, tends to reduce the adjustment costs of external shocks
and thus enables the economy to recover more quickly.6
As our discussion regarding the two competing hypotheses indicates, the openness-crisis
interaction can only be settled empirically. In this study, we examine whether African countries
that are more open to trade and financial flows suffer smaller reductions in output following
external shocks relative to more closed ones. In other words, are open African economies more
likely to accommodate external shocks?

3 Data and methodology
3.1 Data
Following Cavallo and Cavallo (2010) among others, we want to explore the medium tolong-term effects of crises on output growth. To this end, we construct a panel dataset on a5This refers to the situation where developing countries cannot get loans denominated in their own currencies
from international financial markets. Thus, a depreciation/ devaluation of their currencies would make thevalue of their liabilities rise. These balance sheet effects would reduce the net worth of firms.
6
A careful examination of the existing literature, however, indicates that, under fairly standard assumptions,financial openness may in fact result in greater instability (see for example Kim et al. 2012 for a review).

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maximum of 41 African countries and 8 non-overlapping 5-year period averages from 1970-74
through 2005-09. In line with the existing literature, the data is averaged to reduce business cycle
effects. Table A1 in the Appendix provides full definitions and sources of all the variables. The
model we estimate takes the following form:

yit  yit 1 = 0  1 yit 1  1CRit   2 EOit   2 X it  i   t   it ,(1)

where for i = 1,...N and t = 1,...T , y denotes the real GDP per capita for country i at time
t , CRit and EOit denote our measures of crises and economic integration, respectively,  i isa time invariant country-specific fixed effect,  t is a time specific effect and  it is the error
term. We are interested in testing whether the marginal effects of crisis and openness on growth, 1 and  2 , are statistically significant.

The X it is a set of standard control variables, largely drawn from the existing literature,
which include inflation and the share of investment in GDP. In line with the seminal contribution
by Beck et al. (2000), we account for the role of financial development in economic growth. We
use the ratio of liquid liabilities to GDP as an indicator of financial development. We include
population growth to control for the demographic trends of African countries. As suggested byBarro (1996), high population growth can have a negative effect on growth through its impacts
on the dependency ratio and quality of human capital. Finally, we control for the level ofindebtedness since it may play an important role in the relationship between crises and growth.
In particular, we wish to test whether crises are significantly harmful to growth even after
controlling for one of the most important correlates of crises, namely ‘debt overhang’.
We then extend our analysis by allowing the growth effect of crises to vary with the levelof economic integration. We do this by interacting the crises measures with indicators of
openness, as follows:8yit  yit 1 = 0  1 yit 1  1CRit   2 EOit   1 (CRit  EOit )   2 X it i   t   it(2)

A good way to understand how growth reacts to external shocks in countries with varying
levels of openness is to examine the marginal effect from equation (2):( yit  yit 1 )= 1   1EOit
CRit

(3)

Thus, we interpret the signs of the coefficients of CRit and the interaction term as follows: if
1 < 0 and  1 > 0 , this would confirm the hypothesis that openness acts as a crisis buffer,

which would suggest that the adverse effects of crises are decreasing with the level of economic
integration. On the other hand, if 1 < 0 and  1 < 0 , this would confirm the hypothesis that
economic integration can amplify the negative effects of crises on output growth.

Crisis indicators
To identify episodes of sudden stop crises, we closely follow the work of Guidotti et al. (2004)
and Calvo et al. (2004) to define a sudden stop as a fall in the financial account that is at least
one standard deviation below the sample mean and more than 5 percent of the country’s GDP.
However, we impose an additional requirement in that we require the episode to be disruptive.
One way to do this is to follow the procedure by Hutchison and Noy (2006). They focus on
episodes that coincide with other types of crises. Our approach is broader and requires theepisode to coincide with, or be followed by, other forms of financial crises, namely, currency anddebt crises. In this way, our measure of a sudden stop reflects not only changes in the mood of
global capital markets, but also how harmful the episode might be. Hence, we use a dummy
variable that takes 1 if there is a sudden stop in a country during a particular year and 09otherwise.7
We also make use of similar dummy variables capturing the incidence of currency andsovereign debt crises. Our currency crisis measure is based on that of Reinhart and Rogoff(2009), who define it as an annual depreciation (local currency vs US dollar) of 15 percent or
more. Our sovereign debt crisis measure comes from the same source and is defined as a failure
to meet a principal or interest payment on the due date (or specified grace period) including
rescheduling of debt agreements irrespective of the nature of any new terms.8
Following Kaminsky and Reinhart (1999) and Bordo et al. (2001), we also consider theeffects of the joint (‘twin’) occurrence of banking and currency crises on output growth inAfrica. The data on banking crises comes from the dataset by Laeven and Valencia (2008).
Finally, to capture the severity and intensity of financial crises, we construct a composite crisis
index, which can take on a value between 0 and 4, depending on the number of types of crises
encountered by a country in a particular year. For example, in 1992 Nigeria simultaneously
experienced a sudden stop episode with currency, twin, and sovereign debt crises. Hence, weaward Nigeria an index score of 4 for that particular year. We then weigh the index by the shareof each country’s GDP in world output. A similar procedure has been adopted by Reinhart andReinhart (2010). The composite index is our preferred indicator as it captures whether
simultaneously encountering different types of crises has an additional adverse effect on growth,
above and beyond the adverse effect of each crisis individually.

7

Our crisis dummies are defined for each year first and then averaged over five years. That is, they appear as
the ratios of crisis years to total years (5 years) to capture the duration aspect of crises. Our results are robust to
the use of binary indicators.
8
A shortcoming of this crisis-measure in the context of many low-income countries is that it does not capture
the dynamic nature of external crises. For example, prior to the HIPC initiative, interest arrears were applied to
official borrowing which was eventually rescheduled. Moreover, some of the countries that were included in
the HIPC process were deemed to have implemented reform measures during the period in which they wereaccumulating debt arrears. We owe this point to one of the referees.

10Using the definitions and sources detailed above, we identify - out of 1880 observations a total of 202 currency crises, 172 sovereign debt crises, 249 sudden stop episodes and 56 twincrises (banking and currency). Figure 1 shows the distribution of currency crises over time. It
seems that the highest number of currency crises were recorded in 1994, when the CFA franc
was devalued by 50%. The occurrence of sovereign debt crises peaked during the mid to late1980s (Figure 2), while a significant number of countries experienced sudden stop episodes fromthe late 1970s onwards (Figure 3). Twin crises were the least frequent type of crisis during thesample period, occurring mostly in the 1990s (Figure 4). Over the sample period, the three
countries that have encountered most currency crises are Zimbabwe (21 years out of 40 years),
Ghana (20) and Angola (20) while Cape Verde and Liberia have not experienced any. With
regards to sudden stops, the top three are Sierra Leone (23), Mali (21) and Swaziland (20), while
Equatorial Guinea has not experienced any. Finally, the top three sovereign crisis-hit countries
are the Central African Republic (28), Cote d’Ivoire (26) and Angola (20) while around a quarterof the countries have not experienced any sovereign debt crises.
(Figures 1 - 4 here)

Openness indicators
We utilise several measures of economic and financial openness. We use the economic
dimension of the globalisation index created by Dreher (2006).9 It is a weighted index of actual
economic flows (trade, foreign direct investment, portfolio investment and income payments toforeign nationals each measured as a percentage of GDP) and their restrictions (hidden importbarriers, mean tariff rate, taxes on international trade and capital account restrictions). This is our

9This is commonly known as the KOF index as it is hosted by the KOF (Konjunkturforschungsstelle) Swiss
Economic Institute.

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preferred indicator since it captures the degree to which economies are connected to the rest of
the world. As sensitivity tests, we also employ the actual economic flows sub-index from thesame dataset and the share of trade (sum of exports and imports) in GDP, each capturing
different aspects of cross border transactions. To measure financial openness, we use the de jureindex of capital account openness proposed by Chinn and Ito (2008). This measure is the firstprincipal component of four binary dummy variables related to restrictions on cross-borderfinancial transactions.

3.2

Methodology

As a benchmark case, we carry out panel estimations without the interaction of crisis andopenness based on (i) a pooled OLS model without controlling for country and time fixed effects
as well as (ii) a fixed effects model where unobservable country fixed effects and time fixed
effects are included. However, a particular issue of concern in estimating our model (equation 1or 2) is endogeneity bias which may arise from omitted variables, simultaneity or reverse
causality in the relationship between crisis or economic integration and economic growth. To
overcome this, we use the generalised method of moments (GMM) estimators proposed by
Holtz-Eakin et al. (1988) and Arellano and Bond (1991) and further developed by Arellano and
Bover (1995) and Blundell and Bond (1998). In particular, we use the system GMM (SGMM)
dynamic panel estimator, which has been shown to have superior finite sample properties.10

4 Results
4.1 Exploring the data

10

See Baltagi (2013) for technical details.

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Before econometric results are presented, this subsection explores the data to give an overview
of growth performances during crisis episodes. We first conduct a basic event analysis where we
examine whether a crisis event is accompanied by an output loss or growth collapse (e.g.
Eichengreen et al. 1995; Frankel and Rose, 1996). The existing literature defines an output loss
or growth collapse as the deviation of actual output or its growth from its potential trend (Bordo
et al. 2001; Boyd et al. 2005; Gupta et al., 2007). As is standard in the literature, we estimate
output trend based on a 5-year pre-crisis period ending 3 years prior to each crisis event using
Hodrick-Prescott smoothed output series. However, in many cases, we end up with negative
growth trends (Abiad et al. 2009; Angkinand, 2008).11 To solve this, Abiad et al. extend thepre-crisis period back until a positive trend is achieved (10 to 20 years back) while Angkinandsets all the negative 3-year pre-crisis growth rates to zero.
In this study, we opt for an alternative strategy which imposes as few restrictive
assumptions as possible. In particular, we ask a question: How does output growth behave before,
during, and after the crisis period? A simple way to do this is to compare the actual growth rates
in period T (onset of a crisis) to T₋₁,..,T₋₅ (pre-crisis window) and T₊₁,..,T₊₅ (post crisis period).
Figure 5 shows that debt crises tend to be associated with greater output collapses in Africa. This
is in line with Andersson and Karpestam (2014) who found that debt crises have been more
harmful to output growth in Africa than any other types of crisis. Figure 5 suggests that sudden
stop episodes tend to be preceded by a boom and output tends to suffer a small contraction. Ourfinding that sudden stops occur on the back of boom times is consistent with the view thatdeveloping countries tend to experience capital inflow bonanzas during good times (orprocyclicality) to be driven, for instance, by a strong surge in global commodity prices (Reinhart

11

Negative trends would suggest that output falls indefinitely even in the absence of a shock (see Abiad et al.
2009).

13and Reinhart, 2008). Consistent with Reinhart and Rogoff (2009), the figure indicates thatcurrency crises are, on average, associated with mild contractions. In addition, in the case ofcurrency crises, post-crisis growth tends to be higher than the pre-crisis level. This may be theoutcome of the pro-competitive effects of the exchange rates. Unlike in the other cases, growthtends to be poor in the run-up to twin crises, but the onset of crisis itself is not associated with agrowth collapse. Despite diversity across different types of crisis in the relationship between
crisis and growth performance, Figure 5 implies the overall negative relation between crisis andgrowth, which will be investigated in detail in Section 4.2.
(Figure 5 here)
However, it should be emphasised that pooling the growth performance around crisis
episodes across the sample countries only captures the overall trend and does not represent theexperiences of all countries. In an attempt to show how particular countries perform when theyencounter crises, we depict the experiences of selected economies in Figures A1-A4 in
Appendix. As can be seen in these figures, financial crises can coincide with growth collapses(e.g. Guinea-Bissau in 2002). Alternatively, they can occur either during a period of sluggish
growth (e.g. Niger in 1983) or on the back of a good performance (e.g. Ethiopia in 1998).
However, crises can coincide with impressive growth rates (e.g. Mozambique in 1987). It shouldbe thus noted that our econometric estimates for coefficients of key variables show only overall
relationships of key variables and do not necessarily reflect the experience of individual
countries. Table A1 in Appendix reports the summary statistics of all the variables used in theestimations.

4.2

Baseline regression results14

As a starting point to generalise the results on the effects of crisis on growth, we explore theeffects of crisis and openness on economic growth in Table 1 without taking account ofinteraction between crisis and openness. The results in the first five columns are based on thepooled OLS estimator. Across the five regressions, we augment our growth model with the fivedifferent indicators of crisis described above, along with our preferred measure of economic
integration. The estimated coefficients of economic integration are all positive and statisticallysignificant, suggesting that economic integration is associated with better growth performance.
Consistent with the existing literature, a sudden stop crisis is harmful to output growth.
Similarly, the rest of the financial crisis indicators are inversely related to growth, thecoefficients of these variables being statistically significant at the 5% level. In the last fivecolumns of Table 1, we re-estimate the same models using the fixed effects panel estimator tocapture the within-country variation in output growth. The results remain largely the same.
Hence, these findings indicate that crises disrupt economic activity, while openness, perhaps byrelaxing credit constraints and thus improving capital accumulation, is beneficial to economic
growth performance.
(Table 1 here)
In general terms, all the control variables are consistent with our prior expectations. Morespecifically, initial income is statistically significant and negative, confirming the conditional
convergence hypothesis. The estimated coefficients of inflation are negative and statisticallysignificant in the last five columns, implying that macroeconomic instability is linked to low
growth rates. In line with the so-called ‘debt overhang’ hypothesis, we obtain negative andgenerally significant coefficients of external debt. There is no evidence to support the idea that
population growth or financial depth have a significant influence on growth. As expected,

15

investment helps to explain the variation in growth, further underlying the importance of capital
accumulation for economic development.
However, a legitimate concern with these results is that some of the right hand side
variables may be endogenously related to growth. A particular source of endogeneity which may
plague our baseline model is reverse causality. For example, it is likely that the level of
economic integration may change with the growth performance of the economy, so that countries
may open up their current and capital accounts precisely because of improved domestic growth
performance. To overcome these concerns, we re-estimate the baseline regressions in Table 1
using the two-step SGMM. The results are reported in Table 2.
(Table 2 here)
Even if endogeneity concerns are addressed, economic integration retains its positive andsignificant effect on growth. Across all specifications, the coefficients of this variable are
significant at the 1% level, confirming that openness matters for growth in Africa. The results
imply that an increase in trade and investment flows by one percentage point of GDP is, on
average, associated with an increase in real per capita GDP growth rate of 0.22 to 0.25
percentage points, which appear to be substantial. On the other hand, the evidence that crises are
detrimental to economic performance has been further confirmed in Table 2. The results suggestthat currency crisis has a marginally stronger depressing impact on output growth than the othertypes of crises, closely followed by twin crises, sovereign debt and sudden stops. Interestingly,
the coefficient of our composite measure, which captures the intensity with which countries
encounter multiple crises, is somewhat lower than the other crisis indicators but, nonetheless,negative and highly significant.
The three specification tests are all well-behaved. The Hansen test of over-identifying

16

restrictions fails to reject the null that the instruments are valid. Similarly, thedifference-in-Hansen test fails to reject the null that the orthogonality conditions derived fromthe levels equation are appropriate. Finally, the regressions pass the second order serial
correlation test, confirming that there is no second-order serial correlation in the error term of thefirst-differenced equation. Hence, these tests support the validity and consistency of the SGMM
estimator.

4.3

Varying the impact of crises across levels of openness

In order to investigate whether the level of economic integration influences the relationshipbetween crises and growth, we interact the openness variable with our crisis indicators. The
results are summarised in Table 3.
(Table 3 here)
Regression [1] shows that sudden stop episodes have a highly significant negative
association with economic growth. On the other hand, openness has a significant beneficial
effect on economic performance. The coefficient of the interaction term carries a significant
positive sign, suggesting that economic integration mitigates the adverse effects of a sudden stopcrisis. So a highly open economy, such as South Africa with an average openness to GDP ratioof 0.60, would be able to avoid any output losses around sudden stops.12 On the other hand, in aperiod of crisis, the output growth of the least open economy (i.e. Rwanda with an average
openness to GDP ratio of 0.16) would drop by more than three percentage points.13
In regression [2], we consider currency crises, which are found to be inversely related to
12

The overall growth effect following a sudden stop for South Africa would be given by the following
equation;  4.191  (0.07  0.60) 100 = 0.009 (%).
13
Followingthemarginal
effectequation,for
Rwandathisiscalculatedas: 4.191  (0.07  0.16) 100 = 3.071 (%).

17

output growth. When we interact this variable with openness, we find a positive and significant
coefficient estimate. This implies that openness tends to attenuate the negative relationshipbetween currency crises and growth. Economic integration itself retains its positive andsignificant sign. Regression [3] examines how debt crises relate to growth. The coefficient of thistype of crisis is negative and highly significant. This suggests that debt crises, similar to the othertypes of crises, is detrimental to growth. The coefficient of the interaction term is positive andsignificant, indicating that the more an African economy is integrated with the rest of the world,
the weaker the negative association between debt crises and economic performance. The patternis the same across the remaining specifications, the coefficients of our indicators of twin crisisand composite crisis measures both being negative, the latter significantly so at the 1% level.
However, the former is marginally insignificant and likewise its interaction term. The coefficientof the interaction between the composite crisis index and openness is positive and significant.
To sum up, in line with both theoretical and empirical literature, our results show that
financial crises are associated with output losses. Our findings also indicate that the crisis-growth
relationship is conditional on the openness of the country to trade and financial flows. Morespecifically, in open countries, the harmful impacts of crises are lessened. This is not the case inclosed economies. This suggests that open countries tend to experience a smoother adjustment
following an external shock, perhaps driven by the performance of the tradable goods sector. It
could also be the case that countries more integrated with the rest of the world have more room
to manoeuvre by international partners (e.g. trade credits). These opportunities may not be
available to more closed economies.

4.4

Alternative measures of openness18

To ensure that our results are not sensitive to the choice of openness indicator, we use various
other measures that capture the degree to which economies are integrated with the rest of theworld. For the sake of brevity, Table 4 contains only the results for our variables of interest (i.e.
measures of openness, crises and their interaction terms)14. As can be seen in Panel A of Table 4,
our previous findings remain largely robust when we use ‘cross border transactions’ as a measureof openness. However, while the coefficient of this variable is positive, it is only significant in 2
out of the 5 regressions. Nonetheless, our measures of crises retain their expected (negative)
signs and are statistically significant. More importantly, the interaction effects remain positiveand generally statistically significant.
(Table 4 here)
In panel B of Table 4, we apply trade openness as an indicator of economic integrationand the results are broadly in line with our previous findings. The coefficients of trade openness
and crises carry the expected signs and are significant in all specifications, suggesting thatcountries that are relatively more open tend to experience better growth performance than thosethat are more closed. Similarly, crisis has a negative and significant effect on economic growth.
The coefficient of the interaction term is positive and generally significant. Overall, thesefindings tend to support the view that openness can mitigate the negative effects of crises.
We also consider other openness measures that exclusively focus on the capital account.
Interestingly, when we use the Chinn and Ito measure of capital account openness (Panel C) we
find that it is statistically insignificant. This is perhaps not too surprising since this indicator is a‘de jure’ measure, solely focusing on restrictions on the capital account. However, the coefficientestimates of financial crisis and its interaction term with capital account opnness are in line with
our previous results, that is, the former is ngative and the latter is positive and mostly significant.
14

The regressions include all the control variables.

19

In line with theoretical predictions, financial openness is found to reduce the negative impact ofcrises on econmic growth.15

4.5

Further robustness checks

As a further robustness check, we have re-estimated the model either by (i) including a further
control variable, (ii) estimating quantile regressions, and (iii) repeating the main regressions for
sub-samples to test the stability of coefficient estimates of our key explanatory variables. In thesecases, we use our preferred measures of openness and crisis, namely, economic integration(proxied by the economic dimension of the KOF index) and the composite crisis indicator aswell as sudden stops. First, our main results are found to be mostly robust to inclusion of anadditional control variable, such as political or institutional variables.16 17 Second, there could
be parameter heterogeneity across the conditional growth distribution, so that countries in thehigher growth quantiles may respond differently to both crises and economic integration than do
15

As a further extension, we have used, as an alternative to the Chinn-Ito index, (i) the financial openness
indicator of Lane and Milesi-Ferretti (2007) - measuring the external assets and liabilities of economies as ashare of GDP- and (ii) FDI liabilities (Kose et al., 2009). In case of the former, positive and mostly significant
coefficients are found for openness across different specifications, while crisis remains negative andsignificant, except in the case where ‘currency crisis’ is used. The interaction term is positive in all the fivecases, but statistically significant only for ‘sudden stop’ and ‘composite index’. The latter (using FDI liabilities
as an alternative openness measure) suggests that, while crisis exerts a statistically significant negative effecton growth, openness is positive and significant, with their interaction positive and mostly significant. These
results are broadly consistent with the results in Table 4, but will have to be interpreted carefully as bothvariables are rather poor proxies for financial openness in the context of our study.
16
More specifically, we have included (i) political and institutional variables, such as regime type, polity, or
political rights (e.g. Acemoglu et al. 2003; Cavallo and Cavallo, 2010), (ii) reserve holdings or terms of tradeon growth (Li and Ouyang, 2011) , (iii) government size, or (iv) consumption volatility, but these arestatistically insignificant except in the case of reserve holdings. The pattern of our main results is unchanged,
though in the case where reserve holdings or terms of trade is added, economic integration gets statisticallyinsignificant (though its interaction with crisis is positive and significant). Details will be provided on request.
17
Another potentially important issue is that our currency crisis variable may capture high inflation whengenuine currency crises were accompanied by rapid currency depreciation. To test this effect, we have added adummy variable for high-inflation cases defined based on various cut-off points (e.g. 7%; 10%; 15%) or
country-specific historical benchmarks. The results indicate that inclusion of high inflation dummies does notchange the results significantly. We have also tested whether the 1994 CFA devaluation has different effects
from other crises, but we have found that the 1994 CFA devaluation dummy is statistically insignificant. These
results will be furnished on request.

20

countries whose growth rates are in the lower quantiles. To explore this, the first two columns ofTable 5 are based on quantile regressions using composite index of crisis where we report boththe 25 th quantile (low growth) and the 75 th quantile of the growth distribution (corresponding
to “the high growth group”). Interestingly, we find that high growth performers tend to benefit
significantly from openness while poor performers do not as much. In addition, the coefficientestimate of openness for the high growth group is more than twice as large as that for the low
growth group. The results also suggest that the adverse impact of crises on output growth issignificantly different from zero for both groups, but the magnitude is slightly larger for the highgrowth group. More importantly, the coefficient of the interaction term is positive and significant
for both groups. Overall, our main results remain unchanged. With regard to other results, initial
GDP is negative for both groups, but statistically significant only for the high growth group witha coefficient estimate much larger in an absolute term. The speed of convergence is thus
observed generally stronger among the high growth group countries than among the low growthgroup countries.
Thirdly, we have carried out a number of sensitivity test by estimating the same model
for various subsets of countries (columns [3]-[8]). The negative link between crises and growththat we have found so far could be due to the presence of resource-rich countries since these may
be more prone to crises but also more integrated with the rest of the world. Hence, we examine
whether our central findings survive if we focus on resource-poor countries (column [3]), or
resource-rich countries (column [4]). In the former, we have removed the countries whose
exports are dominated by oil (Equatorial Guinea, Congo Rep., Angola, Gabon, Chad andNigeria) and minerals (Mauritania and Zambia), and in the latter only these countries are used.18

18

We owe this point as well as the selection of sub-groups of countries to one of the referees.

21Because of the small sample size, the result for resource rich countries will have to be interpretedwith caution as this case passes specification tests only marginally. It is noted, however, that themain results are unchanged for both resource-rich and resource-poor countries - with a negativeand statistically significant coefficient estimate for crises and a positive and significant estimate
for openness as well as for the interaction term. Economic integration has a relatively larger
effect on growth of resource-rich countries, while their integration to the rest of the economy will
mitigate the negative effect of crisis to a larger extent as those countries tend to have closer links
with trade partners. Initial GDP carries a more negative and significant coefficient, suggesting agenerally faster convergence for resource-rich countries than for resource-poor countries.
As previously emphasised by a number of studies (e.g. Loayza and Ranciere, 2006), oneof the most important transmission channels between crises and output growth is the financial
system. Accordingly, it could be the case that our results are driven by countries with moredeveloped financial systems. The regression results reported in column [5] are based on a
sub-sample of countries with relatively weak financial systems. Again, the interaction term
between crises and openness is positive and significant, implying that openness tends to lessen
the disruptive effects of crisis on output growth, even in the absence of sophisticated financial
systems. We also examine whether our findings are true for those countries with greater
restrictions on trade. This sample of ‘closed’ economies is selected based on levels of tariffs(column [6]). As the result shows, the main results remain unchanged. It should be noted that thesize of coefficient of the interaction term is relatively larger for both countries with “weakfinance” and those with “closed economies”. That is, the relative advantages of opening up the
economy to the rest of the world in mitigating the negative effects of external shocks will be22larger for these countries.19
An important question is whether crises were caused by withdrawals of official lending toAfrican governments, or by periodic deteriorations of confidence in private international
financial markets. It should be noted, however, that due to data limitations, we cannot distinguishcrises associated with official, as opposed to private international finance. Given data limitations,
we have carried out sensitivity analyses for “sudden stops” as these are likely to be associatedmainly with private international finance that would normally be pertinent to relatively rich or
resource-rich countries (columns [7]-[9]). As expected, richer countries benefit more from
economic integration, but sudden stops affect more negatively low growth groups than highgrowth groups with the interactive effect larger for the former in the absolute term ([7] and [8]).
Resource poor countries tend to be hit severely by sudden stops. If sudden stops are mainly
associated with private international finance, it will be safe to conclude that low-growthcountries or resource poor countries tend to be negatively affected by crises and economic
integration will mitigate the negative effects regardless of whether the crises originted frompublic or private finance. The pattern of the coefficient estimates of initial GDP is alsounchanged. For instance, faster convergence is found for the high growth group, than for the low
growth group. Hence, our main conclusion appears to be robust regardless of the sources offinance and the definitions of crisis.20
In sum, we find that financial crises are associated with output losses. However, thisnegative effect decreases with the level of openness regardless of specifications, or of selection
19

We have also divided our sample countries into two groups, based on the prevailing exchange rate regime
as the exchange rate regime of a country may influence the relationship between crises and output (e.g. Esaka,2010). The main results are unchanged for countries with ‘semi flexible’ regimes (i.e. those with either
crawling/managed floated or pure floated). The interaction term loses its significance for those with‘fixed/pegged’ exchange rates, while crisis is negative and significant.
20
A formal analysis to distinguish crises in terms of whether they are oriented in official lending orinternational private finance is an important avenue for future research.

23

of sub-samples. Our results also suggest that economic integration, perhaps by relaxing credit
constraints, helps economies to overcome the adverse effects of financial crises on economic
performance. The beneficial effects of openness on growth in Africa is in line with the findingsof Brückner and Lederman (2012) and Chang and Mendy (2012).
With respect to financial openness, Fowowe (2008) shows that there is a significant androbust positive relationship between economic growth and financial liberalisation policies in his
sample of Sub-Saharan African countries. Similarly, Ahmed (2011) provides evidence that
financial integration has had a positive (albeit not statistically significant) direct impact on output
growth in Africa. However, he finds that financial openness in Africa has had a positive androbust effect on African financial markets and thereby indirectly benefitted their growth
performance. Lastly, the SGMM diagnostics are satisfactory throughout the sensitivity analysis.

5. The main findings in a broader context
Our first main result is that international economic integration has been beneficial to our sampleof African countries. This is in line with long held view in economics that increased international
trade can propel countries to a high-growth trajectory. Standard trade theory, for example,
postulates that trade openness is associated with static gains as it provides greater scope for theaccumulation of human and physical capital. In particular, openness can facilitate economies toallocate their resources more efficiently by providing market platforms which allow economies
of scale and division of labour to take place – increasing total factor productivity. Moreover,endogenous growth theories (e.g. Romer, 1994) predict that opening up trade enables countries
to acquire new technologies, skills, knowledge and various other positive externalities which canbring about dynamic gains resulting in higher economic growth.

24

Similarly, an extensive theoretical literature identifies various direct and indirect channels
through which financial openness can foster higher productivity and improve economic
performance. Kose et al. (2009) contend that financial integration can increase capital
accumulation by relaxing credit constraints and augmenting domestic resources. In addition,
openness to financial flows can promote more efficient capital allocation as a result of increasedrisk-sharing opportunities which enables firms to undertake more risky but high-returninvestments (Obstfeld, 1994). As the volume of capital increases, the cost of capital should fallsince the domestic economy becomes more liquid (Prasad et al. 2003).
The second main finding of this paper is that crises have been harmful to output growthin African economies, presumably due to their adverse effects on domestic capital formation,
labour market, exchange rates, asset prices, aggregate demand, and total factor productivity. Asemphasised, for instance, by Korinek (2011), crises cause self-reinforcing ‘financial
amplification’ effects in which countries can be caught in a vicious circle of falling prices(exchange rates and asset prices), deteriorating balance sheets and decreasing aggregate demand.
More specifically, crises, particularly those that come in the form of capital reversals, sudden
stops and currency crises, are associated with sharp falls in the exchange rate and asset prices.
This, in turn, deteriorates domestic firms’ balance sheets by undermining their collateral value
and net worth, further reducing their ability to borrow and invest owing to reduced access tocredit. These effects tend to be amplified in environments where there is credit scarcity, highliability dollarization and financial market imperfections. In crisis-hit countries, lack of credit
availability reduces aggregate demand by tightening the budget constraints of agents, so
decreasing their consumption and investment levels. The tendency of crises to undermine
investor confidence can arise, not only from lack of credit availability, but also from increased25risk and uncertainty. In addition, in Keynesian settings where prices/wages are downward sticky,
depressed aggregate demand is associated with higher unemployment and output losses(Reinhart and Calvo, 2000). Furceri and Mourougane (2012) highlight that, on the one hand,crises may reduce total factor productivity through their negative impact on innovation andresearch and development as these tend to be higher in good times. On the other hand, totalfactor productivity may increase in crisis situations if firms, in an attempt to minimise losses andretain competitiveness, restructure and/or improve their X-efficiency.
Our third and final key result is that openness tends to mitigate the adverse effects ofcrises in Africa. This is in line with the predictions of standard Mundell-Flemming type models
and sticky-price open economy models. Similar findings have been reported elsewhere (e.g.
Guidotti et al. 2004; Edwards, 2004; Cavallo and Frankel, 2008). However, our study is the first
to investigate the crisis-openness interaction in the context of African economies.

6.

Concluding Remarks

In this study, we use a panel dataset covering a large number of African countries to analyse therelationship between crises and growth in a comprehensive manner. Focusing on four different
types of financial crisis, we provide evidence showing that external shocks have been detrimental
to economic growth of African countries. The central findings of this study are in line with thetheoretical view that crises disrupt economic activity. Our empirical results add to the growing
empirical evidence that crises undermine economic growth (Cavallo and Cavallo, 2010; Joyce
and Nabar, 2009).
In line with the existing literature (e.g. Brückner and Lederman, 2012; Chang andMendy, 2012), we find a robust positive link between economic openness and growth26performance in Africa. Our results can be generalised to measures of financial openness. A
variety of mechanisms could rationalise this result - the most plausible being that financial
openness may have had a robust beneficial effect on African financial markets and thus
indirectly promoted growth (e.g. Ahmed, 2011).
In an attempt to identify the specific channels through which crises affect output growth,
we test the hypothesis that the level of economic integration of the crisis-hit country is important.
We find that crises have had a more disruptive effect on growth in countries with lower levels of
openness. We postulate that openness lessens the adjustment costs associated with external
crises. This implies that once an African economy reaches a certain level of financial andeconomic openness, the negative effects of crises would be minimised, presumably because thecountry would be in a position to keep the fall in aggregate demand in check.

References
Abiad, A. G., R. Balakrishnan, P. Koeva Brooks, D. Leigh, and I. Tytell (2009) ‘What’s theDamage? Medium-Term Output Dynamics after Banking Crises’, IMF working papers, 1-37.
Acemoglu, D., S. Johnson, J. Robinson, and Y. Thaicharoen (2003) ‘Institutional causes,
macroeconomic symptoms: Volatility, crises and growth’, Journal of Monetary Economics,
50(1), 49-123
Agénor, P. R. (2004) ‘Does globalization hurt the poor? International Economics and Economic
Policy’, 1(1), 21-51.
Aghion, P., P. Bacchetta, and A. Banerjee (2001) ‘Currency crises and monetary policy in an
economy with credit constraints’, European Economic Review, 45(7), 1121-50.
Aguiar, M. (2005) ‘Investment, devaluation, and foreign currency exposure: The case of27

Mexico’, Journal of Development Economics, 78(1), 95-113.
Ahmed, A. D. (2011) ‘International financial integration, investment and economic performancein Sub-Saharan African countries’, Global Economy Journal, 11 (4) 1-26.
Andersson, F. N., and P. Karpestam (2014) Short and Long Term Growth Effects of Financial
Crises. In M. Gallegati and W. Semmler (eds.), Wavelet Applications in Economics andFinance Vol.20 (pp. 227-248), Springer International Publishing Switzland.
Angkinand, A.P., (2008) ‘Output Loss and Recovery from Banking and Currency Crisies:
Estimation Issues’, mimeo., University of Illinois, Springfield.
Arellano, M. and S. Bond (1991) ‘Some tests of specification for panel data: Monte Carloevidence and an application to employment equations’, The Review of Economic Studies,
58(2), 277-97.
Arellano, M., and O. Bover (1995) ‘Another look at the instrumental variables estimation oferror-components models’, Journal of Econometrics, 68(1), 29-51.
Arestis, P. (2005) ‘Financial liberalization and the relationship between finance and growth’,
University of Cambridge, Centre for Economic and Public Policy (CEPP) Working paper,(05/05).
Baltagi, B. H. (2013) Econometric Analysis of Panel Data, 5th Edition, John Wiley & Sons,
Chichester.
Barro, R. (1996) ‘Determinants of economic growth: A cross-country study’, NBER Working
Paper, 5698.
Beck, T., R. Levine, R and N. Loayza (2000) ‘Finance and the sources of growth’, Journal of
financial economics, 58(1), 261-300.
Blundell, R., and S. Bond (1998) ‘Initial conditions and moment restrictions in dynamic panel28

data models’, Journal of Econometrics, 87(1), 115-43.
Bordo, M., B. Eichengreen, D. Klingebiel, and M. S. Martinez-Peria (2001) ‘Is the crisis problem
growing more severe?’, Economic policy, 16(32), 51-82.
Borensztein, E., and U. Panizza (2009) ‘The costs of sovereign default’, IMF Staff Papers, 56(4), 683-741.
Boyd, J. H., S. Kwak, and B. Smith (2005) ‘The real output losses associated with modernbanking crises’, Journal of Money, Credit and Banking, 37(6), 977-999.
Brückner, M., and D. Lederman (2012) ‘Trade causes growth in Sub-Saharan Africa’, WorldBank Policy Research Working Paper, 6007.
Calvo, G. A. (1998) ‘Capital flows and capital-market crises: The simple economics of sudden
stops’, Journal of Applied Economics, 1(1), 35-54.
Calvo, G. A., A. Izquierdo, and E. Talvi (2003) ‘Sudden stops, the real exchange rate, and fiscal
sustainability: Argentina’s lessons’, NBER Working Paper, 9828.
Calvo, G., A. Izquierdo, and L. F. Mejia (2004) ‘On the empirics of sudden stops: The relevanceof balance-sheet effects’, NBER Working Paper, 10520.
Cavallo, A. F., and E. A. Cavallo (2010) ‘Are crises good for long-term growth? The role ofpolitical institutions’, Journal of Macroeconomics, 32(3), 838-57.
Cavallo, E. A., and J. A. Frankel (2008) ‘Does openness to trade make countries more
vulnerable to sudden stops, or less? Using gravity to establish causality’, Journal ofInternational Money and Finance, 27(8), 1430-1452.
Chang, C. C., and M. Mendy (2012) ‘Economic growth and openness in Africa: What is theempirical relationship?’ Applied Economics Letters, 19(18), 1903-07.
Chinn, M. D., and H. Ito (2008) ‘A new measure of financial openness’, Journal of29Comparative Policy Analysis, 10(3), 309-22.
Dreher, A. (2006) ‘Does globalization affect growth? Evidence from a new index ofglobalization’, Applied Economics, 38(10), 1091-110.
Easterly, W., R. Islam and J. E. Stiglitz (2001) ‘Shaken and stirred: Explaining growthvolatility’, In Annual World Bank Conference on Development Economics (Vol. 191).
Edwards, S. (2004) ‘Financial openness, sudden stops and current account reversals’, NBER
Working Paper, 10277.
Eichengreen, B., A.K. Rose, and C. Wyplosz (1995) ‘Exchange market mayhem: theantecedents and aftermath of speculative attacks’, Economic policy, 10(21), 249-312.
Esaka, T. (2010) ‘Exchange rate regimes, capital controls, and currency crises: Does the bipolar
view hold?’, Journal of International Financial Markets, Institutions and Money, 20(1),91-108.
Fowowe, B. (2008) ‘Financial liberalization policies and economic growth: Panel data evidence
from Sub-Saharan Africa’, African Development Review, 20: 549–74.
Frankel, J. A., and Rose, A. K. (1996). ‘Currency crashes in emerging markets: An empiricaltreatment’, Journal of International Economics, 41(3), 351-366.
Furceri, D., and A. Mourougane (2012) ‘The effect of financial crises on potential output: newempirical evidence from OECD countries’, Journal of Macroeconomics, 34 (3), 822-32.
Guidotti, P. E., F. Sturzenegger, A. Villar, J. de Gregorio and I. Goldfajn (2004) ‘On theconsequences of sudden stops’, Economia, 4 (2), 171-214.
Gupta, P., D. Mishra, and R. Sahay (2007) ‘Behavior of output during currency crises’, Journalof International Economics, 72(2), 428-450.
Holtz-Eakin, D., W. Newey and H. S. Rosen (1988) ‘Estimating vector autoregressions with30

panel data,’ Econometrica, 56(6), 1371-1395.
Hutchison, M. 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(1), 225-48.
Joyce, J. P., and M. Nabar (2009) ‘Sudden stops, banking crises and investment collapses inemerging markets’, Journal of Development Economics, 90(2), 314-22.
Kaminsky, G. L., and C. M. Reinhart (1999) ‘The twin crises: The causes of banking andbalance-of-payments problems’, American Economic Review, 89(3), 473-500.
Kim, D. H., S. C. Lin and Y. B. Suen (2012) ‘Dynamic effects of financial openness on
economic growth and macroeconomic uncertainty’, Emerging Markets Finance and Trade,48(1), 25-54.
Kose, M., E. S. Prasad and M. E. Terrones (2009) ‘Does openness to international financial
flows raise productivity growth?’, Journal of International Money and Finance, 28(4),554-80.
Korinek, A. (2011) ‘The new economics of prudential capital controls: A research agenda’,
IMF Economic Review, 59(3), 523-61.
Laeven, L., and F. Valencia (2008) ‘Systemic banking crises: A new database’, IMF Working
Papers, 1-78.
Lane, P. R. (2001) ‘The new open economy macroeconomics: A survey’, Journal ofInternational Economics, 54(2), 235-266.
Lane, P. R., and G. M. Milesi-Ferretti (2007) ‘The external wealth of nations mark II: Revisedand extended estimates of foreign assets and liabilities, 1970–2004’, Journal ofInternational Economics, 73(2), 223-50.

31

Li, J., and A. Y. Ouyang (2011) ‘Currency crises: Can high reserves offset vulnerable
fundamentals?’, Applied Economics, 43(16), 2055-69.
Loayza, N. V., and R. Ranciere (2006) ‘Financial development, financial fragility, and growth’,
Journal of Money, Credit and Banking, 38(4), 1051-1076.
Milesi-Ferretti, G. M. M., and A. Razin (2000) ‘Current account reversals and currency crises,
empirical regularities’, in P. Krugman (ed.) Currency crises (pp. 285-326). University of
Chicago Press.
Obstfeld, M. (1994) ‘Risk-Taking, Global Diversification, and Growth’, American Economic
Review, 85, 1310-29.
Prasad, E., K. Rogoff, S. J. Wei and M. A. Kose (2003) ‘Effects of financial globalisation on
developing countries: Some empirical evidence’, Economic and Political Weekly, XXXVIII
No. 41, October 11, 4319-4330.
Reinhart, C., and G. Calvo (2000) ‘When capital inflows come to a sudden stop: Consequences
and policy options’ in P.B. Kenen, A.K. Swoboda (eds.), Key Issues in Reform of theInternational Monetary and Financial System, Washington D.C.: International Monetary
Fund.
Reinhart, C. M., and V. R. Reinhart (2010) ‘After the fall’, NBER Working Paper, 16334.
Reinhart, C. M., and V. R. Reinhart (2008) ‘Capital flow bonanzas: An encompassing view ofthe past and present’, NBER Working Paper, 14321.
Reinhart, C. M., and K. S. Rogoff (2009) This time is different: Eight centuries of financial
folly, New Jersey: Princeton University Press.
Ripoll-i-Alcón, J. (2010) ‘Trade integration as a mechanism of financial crisis prevention’,International Advances in Economic Research, 16(2), 149-64.

32

Romer, P. M. (1994) ‘The origins of endogenous growth’, Journal of Economic Perspectives,
8(1), 3-22.
Roodman, D. (2006) ‘How to do xtabond2: An introduction to difference and system GMM in
Stata’, Center for Global Development working paper, (103).
Roodman, D. (2009) ‘A note on the theme of too many instruments’, Oxford Bulletin of
Economics and Statistics, 71(1), 135-158.
Rose, A. K. (2005) ‘One reason countries pay their debts: Renegotiation and international
trade’, Journal of development economics, 77(1), 189-206.
Sachs, J. (1985) ‘External debt and macroeconomic performance in Latin America and East
Asia’, Brookings Papers on Economic Activity 2, 523-73.
Schimmelpfennig, A., N. Roubini and P. Manasse (2003) ‘Predicting sovereign debt crises’,
IMF Working Papers 03/221, International Monetary Fund.
Stiglitz, J. E. (2000) ‘Capital market liberalization, economic growth, and instability’, World
Development, 28(6), 1075-86.
Windmeijer, F. (2005) ‘A finite sample correction for the variance of linear efficient two-step
GMM estimators’, Journal of econometrics, 126(1), 25-51.
Zagha, R., and G. T. Nankani (eds.) (2005) Economic Growth in the 1990s: Learning from a
Decade of Reform, Washington D.C.: World Bank Publications.

33

Table 1: Crises, Economic Integration and Growth in Africa – Pooled OLS or Fixed-Effects Model
Dependent variable: GDP per capita growth

Economic integration
Sudden stop crisis

[1]
0.068
[0.018]***-1.256[0.395]***

[2]
0.064
[0.018]***Pooled OLS
[3]
0.061
[0.018]***

[4]
0.057
[0.018]***

[5]
0.064
[0.018]***

[6]
0.068
[0.032]**-1.401[0.370]***-0.981[0.431]**

Currency crisis

[7]
0.055
[0.033]*[9]0.053[0.038][10]
0.063[0.031]**-1.412[0.448]***-1.699[0.493]***

Sovereign debt crisis

Fixed Effects
[8]
0.064
[0.033]*-1.521[0.476]***-1.422[0.577]**

Twins-1.588[0.553]***-0.686[0.172]***Composite crisis index-0.816[0.176]***

Controls
Log(initial GDP)Log (1+inflation)
Investment/GDP
External debt/GDP
Financial depth/GDP
Population growth
Constant-0.603[0.257]**0.294[0.354]0.07[0.024]***-0.012[0.003]***
1.467[1.234]-0.112[0.160]2.366-0.666[0.259]**0.471[0.380]0.073[0.024]***-0.012[0.003]***2.087[1.258]*-0.078[0.162]1.661-0.473[0.261]*0.344[0.354]0.067[0.024]***-0.01[0.003]***
1.398[1.231]-0.034[0.160]0.804-0.636[0.259]**0.315[0.360]0.077[0.024]***-0.011[0.003]***1.951[1.248]-0.11[0.161]
2.312-0.538[0.255]**0.496[0.357]0.063[0.024]***-0.011[0.003]***1.644[1.217]-0.052[0.158]1.038-3.745[1.075]***-1.111[0.547]**0.109[0.031]***-0.011[0.005]**0.1[2.188]2.409[2.203]2.148-3.952[1.106]***-0.983[0.578]*0.11[0.031]***-0.011[0.004]**0.785[2.330]2.244[2.146]
4.735

-3.45
[1.066]***-0.924[0.532]*0.104[0.031]***-0.009[0.004]**-0.036[2.301]
2.782[2.156]-3.675-3.672[0.973]***-0.939[0.480]*0.106[0.030]***-0.009[0.006]0.788[2.477]2.171[2.120]3.399-3.493[1.050]***-0.983[0.533]*0.105[0.030]***-0.009[0.005]*-0.259[2.269]1.923[2.142]5.032[2.449][2.510][2.494][2.473][2.448][22.727][22.439][22.416][20.827][22.174]

Observations
229
229
229
229
229
229
229
229
229
229
R2
0.31
0.3
0.32
0.3
0.33
0.37
0.36
0.35
0.35
0.4
Note: The dependent variable is GDP per capita growth (% p.a.). The estimates are based on the pooled OLS estimator or the fixed-effects estimator with robust standard errors in brackets, *, **, ***indicate significance at 10%, 5% and 1% respectively, time effects included but not reported.

34

Table 2: Crises, Economic Integration and Growth in Africa - System-GMM
Dependent variable: GDP per capita growth
Economic integration
Sudden stop crisis

[1]
0.248
[0.052]***-1.478[0.439]***Currency crisis

[2]
0.226
[0.047]***

[3]
0.268
[0.062]***

[4]
0.244
[0.057]***

[5]
0.216
[0.065]***-1.688[0.507]***

Sovereign debt crisis-1.519[0.540]***Twin crises-1.528[0.581]***

Composite crisis index-0.999[0.237]***

Controls
Log(initial GDP)

-3.159-2.395-4.293-3.153-2.44[1.344]**[0.973]**[1.227]***[1.411]**[1.073]**Log(1+inflation)-1.425-0.820-1.536-1.210-1.084[0.768]*[0.709][0.924]*[0.735][0.639]*Investment/GDP
0.123
0.147
0.071
0.117
0.126
[0.073]*[0.065]**[0.084][0.080][0.076]*External debt/GDP-0.014-0.011-0.017-0.014-0.009[0.011][0.008][0.008]**[0.007]**[0.011]
Financial Depth/GDP
4.674
2.465
7.115
5.223
3.557
[3.336][2.856][3.218]**[3.753][3.291]
Population growth
0.337
0.495
0.003
0.468
0.490
[0.659][0.612][0.835][0.668][0.525]Constant
8.924
1.931
18.5586.851
3.427[12.755][11.022][13.752][13.516][9.807]
Observations
229
229
229
229
229
# Instruments
32
32
32
32
32
# Countries
37
37
37
37
37
Hansen test
0.620
0.621
0.537
0.595
0.595
Diff Hansen test
0.739
0.508
0.684
0.487
0.537
AR (1) test
0.008
0.005
0.010
0.005
0.005
AR (2) test
0.894
0.808
0.895
0.771
0.771
Note: Dependent variable is GDP per capita growth (% p.a.). The estimates are based on the two-step System-GMM estimator with Windmeijer
finite sample correction. AR(1) and AR(2) are respectively Arellano-Bond's 1st and 2nd autocorrelation tests. The Hansen J-statistic reports thep-values for the null of instrument validity. The Diff-in-Hansen reports the p-values for the validity of the additional moment restriction for the
System GMM. Time fixed effects included but not reported. *, **, *** denote significance at 10, 5 and 1%, respectively.

Table 3: Growth effects of crises and interaction with economic integration
Dependent variable: GDP per capita growth
Economic integration
Sudden stops[1]
Sudden stops
0.215[0.061]***-4.191[1.600]***Currency crises[2]
Currency
0.202[0.050]***

[3]
Debt
0.256
[0.067]***

[4]
Twin
0.228
[0.058]***-3.434[1.425]**

Sovereign debt crises-5.638[1.613]***

Twin crises-2.970[1.981]

Composite crises index
Integration * crisis
Controls
Log(initial GDP)

[5]
Composite
0.188[0.058]***0.070[0.033]**0.054[0.031]*

0.111[0.049]**0.041[0.044]-1.899[0.669]***0.028[0.016]*-3.714[1.212]***-2.814[1.018]***-4.848[1.379]***-3.585[1.340]***-3.633[1.303]***35

Log(1+inflation)-1.390-0.531-1.884-1.176-1.304[0.778]*[0.852][0.774]**[0.579]**[0.681]*
Investment/GDP
0.131
0.130
0.081
0.118
0.082
[0.077]*[0.071]*[0.081][0.084][0.074]External debt/GDP-0.017-0.012-0.018-0.018-0.013[0.008]**[0.007]*[0.008]**[0.007]***[0.009]Financial Depth/GDP
5.734
4.348
7.461
5.699
6.973
[2.622]**[2.741][4.878][3.511][3.105]**Population growth
0.174
0.366
-0.5550.101
0.086[0.658][0.664][1.074][0.744][0.724]
Constant
14.7375.688
28.37913.60416.556[11.456][11.411][16.608]*[13.450][13.753]
Observations
229
229
229
229
229
# Instruments
34
34
34
34
34
# Countries
37
37
37
37
37
Hansen test
0.719
0.569
0.477
0.426
0.702
Diff Hansen test
0.801
0.752
0.536
0.488
0.680
AR (1) test
0.005
0.003
0.011
0.004
0.004
AR (2) test
0.893
0.790
0.876
0.497
0.826
Note: Dependent variable is GDP per capita growth (% p.a.). The estimates are based on the two-step System-GMM estimator with Windmeijer
finite sample correction. AR(1) and AR(2) are respectively Arellano-Bond's 1st and 2nd autocorrelation tests. The Hansen J-statistic reports thep-values for the null of instrument validity. The Diff-in-Hansen reports the p-values for the validity of the additional moment restriction for the
System GMM. Time fixed effects included but not reported. *, **, *** denote significance at 10, 5 and 1%, respectively.

Table 4: Growth effects of crises and interaction with openness
Dependent variable: GDP per capita growth
Panel A:
Cross border transactions
Cross border transactions
CrisisOpenness * crisis
Specification tests
Observations# Instruments/ countries
Hansen test
Diff Hansen test
AR (1) test
AR (2) test
Panel B:
Trade openness
Trade openness
CrisisOpenness * crisis
Specification tests
Observations# Instruments/ countries
Hansen test
Diff Hansen test
AR (1) test
AR (2) test
Panel C:
Capital account openness
Capital account openness
CrisisOpenness * crisis

Crisis type and interaction of economic integration with
[1]
[2]
[3]
Sudden stop
Currency
Debt
0.070
0.044
0.113
[0.053][0.034][0.045]**-5.359-5.747-6.642[1.972]***[1.661]***[2.772]**
0.075
0.090
0.107
[0.036]**[0.035]***[0.063]*
225
219
219
34/36
34/35
34/35
0.424
0.526
0.433
0.628
0.655
0.643
0.001
0.001
0.003
0.866
0.680
0.854
Crisis type and interaction of economic integration with
[6]
[7]
[8]
Sudden stop
Currency
Debt
0.052
0.052
0.066
[0.021]**[0.018]***[0.022]***-3.223-5.296-5.779[1.518]**[1.674]***[2.874]**
0.024
0.060
0.051
[0.017][0.026]**[0.039]
253
247
247
36/41
36/40
36/40
0.151
0.179
0.228
0.755
0.825
0.869
0.001
0.000
0.001
0.875
0.614
0.778
Crisis type and interaction of economic integration with
[1]
[2]
[13]
Sudden stop
Currency
Debt
0.361-0.0790.178[0.263][0.412][0.403]-2.007-1.784-3.005[0.489]***[0.473]***[1.647]*
0.603
1.429
-0.908[0.357]*[0.539]***[1.558]

36

[4]
Twins
0.107
[2.600]**-4.363[2.400]*0.0462[1.170][5]Composite
0.074[0.046]-2.198[0.722]***0.022[0.013]*

225
34/36
0.255
0.285
0.001
0.437

225
34/36
0.528
0.605
0.001
0.861

[9]
Twins
0.059
[0.019]***-3.499[1.503]**0.032[0.019]*[10]
Composite
0.042[0.021]**-1.971[0.735]***0.016[0.008]**

253
36/41
0.215
0.784
0.001
0.511

253
36/41
0.165
0.755
0.000
0.933

[14]
Twins
0.158
[0.436]-2.106[0.819]**0.669[0.559][15]
Composite
0.158[0.323]-1.093[0.162]***0.346[0.189]*

Specification tests
Observations
249
243
243
249
249
# Instruments/ countries
34/41
34/40
34/40
34/41
34/41
Hansen test
0.354
0.374
0.416
0.280
0.286
Diff Hansen test
0.267
0.498
0.480
0.263
0.172
AR (1) test
0.001
0.001
0.001
0.001
0.000
AR (2) test
0.666
0.981
0.752
0.389
0.791
Note: Dependent variable is GDP per capita growth (% p.a.). The estimates are based on the two-step System-GMM estimator with Windmeijer
finite sample correction. AR(1) and AR(2) are respectively Arellano-Bond's 1st and 2nd autocorrelation tests. The Hansen J-statistic reports thep-values for the null of instrument validity. The Diff-in-Hansen reports the p-values for the validity of the additional moment restriction for the
System GMM. Time fixed effects included but not reported. *, **, *** denote significance at 10, 5 and 1%, respectively.

37

Table 5: Sensitivity analysis
Dependent variable: GDP per capita growth
Economic integration
Composite crisis index
Openness*composite index[1]
25th quantile
0.024[0.022]-1.278[0.347]***0.020[0.009]**[2]75th quantile
0.054[0.026]**-1.464[0.423]***0.021[0.011]*[3]
Resource poor
0.107
[0.058]*-1.896[0.632]***0.023[0.014]*[4]
Resource rich
0.162
[0.097]*-2.614[0.930]***0.055[0.023]**

Sudden stops
Ec. integration * Sudden stops
Controls
Log(initial GDP)

-0.406[0.264]0.481[0.288]*0.045[0.023]**-0.011[0.002]***2.541[1.054]**-0.152[0.142]1.485[2.338]229-1.194[0.218]***0.025[0.327]0.098[0.023]***-0.015[0.003]***2.233[1.229]*-0.172[0.157]8.215[2.562]***229[5]Weak finance
0.044[0.077]-2.953[0.745]***0.056[0.019]***[6]
Closed economies
0.093[0.041]**-2.982[0.675]***0.046[0.020]**[7]
25th quantile
0.015[0.022][8]75th quantile
0.052[0.021]**[9]
Resource poor
0.126
[0.059]**-4.667[0.966]***0.089[0.024]***-3.126[0.975]***0.053[0.023]**-4.253[1.580]***0.067[0.033]**-1.933-2.751-3.349-2.139-0.547-1.016-1.851[0.941]**[1.054]***[1.216]***[0.730]***[0.303]*[0.197]***[0.917]**Log(1+ inflation)-0.505-0.585-1.660-0.4240.442
0.014-0.969[0.559][0.702][0.351]***[0.653][0.296][0.276][0.623]
Investment/GDP
0.098-0.015
0.092
0.080
0.047
0.116
0.083
[0.062][0.097][0.092][0.049][0.026]*[0.021]***[0.055]External debt/GDP-0.013-0.026-0.011-0.018-0.011-0.016-0.015[0.006]**[0.010]***[0.007][0.007]**[0.004]***[0.003]***[0.007]**
Financial depth/GDP
4.060-27.872-5.027
0.458
3.336
1.924
3.917
[2.129]*[8.181]***[6.550][5.097][1.265]***[1.152]*[2.020]**Population growth
0.136-2.249-0.307-0.164-0.179-0.1400.258[0.317][1.715][0.597][0.363][0.163][0.145][0.300]Constant
7.397
39.24827.57913.412
2.698
6.378
6.587
[6.784][20.818][10.209][4.851][2.511][2.128][6.311]
Observations
200
29
113
136
229
229
200
# Instruments
30
29
24
34
41
# Countries
31
8
27
33
31
Hansen test
0.805
0.100
0.850
0.714
0.810
Diff Hansen test
0.806
0.100
0.734
0.647
0.858
AR (1) test
0.004
0.062
0.009
0.005
0.006
AR (2) test
0.935
0.224
0.585
0.861
0.979
Note: Dependent variable is GDP per capita growth (% p.a.). The estimates are based on the two-step System-GMM estimator with Windmeijer finite sample correction. AR(1) and AR(2) arerespectively Arellano-Bond's 1st and 2nd autocorrelation tests. The Hansen J-statistic reports the p-values for the null of instrument validity. The Diff-in-Hansen reports the p-values for the validity of
the additional moment restriction for the System GMM. Time fixed effects included but not reported. *, **, *** denote significance at 10, 5 and 1%, respectively.

38

Figure 1a

Figure 2

Figure 339

Figure 4

Figure 5 Average growth performance around crisis periods in Africa40Appendix:
Table A1. Variable definitions and sources
Variable

Obs

Mean

Std. Dev.

Growth

358

1.2

4.46

Definition and Source
Real per capita GDP growth rate. World Bank (2011): WDI

Initial income (ln)

354

6.19

1.00

First value of real per capita income for each 5-year period. World Bank (2011): WDI

Population growth

384

2.51

0.05

World Bank (2011): WDI

Sudden stop crisis

384

0.33

0.02

Own calculation based on a modified version of Calvo et al. (2004). See text for description

Currency crisis

376

0.33

0.02

Reinhart and Rogoff (2009). See text for description

Sovereign debt crisis

376

0.17

0.02

Reinhart and Rogoff (2009). See text for description

Twin crisis

384

0.13

0.02

Joint occurrence of banking and currency crises. The data on banking crises is from Laeven and Valencia (2008)

Composite crisis index

38

1.05

0.06

Own calculation based on types of crises encountered in a given year, weighted by each country's share in world output.

Investment

340

20.95

9.78

Gross capital formation as a % of GDP. World Bank (2011): WDI

Debt

343

80.06

103.36External borrowing as a % of GDP. World Bank (2011): WDI

Financial depth

282

0.29

0.19

Captured by Liquid liabilities as a % of GDP. World Bank (2011): WDI

Inflation

3.58

3.06

0.69

Measured as ln (1+ inflation (%)/100) where inflation is captured by CPI. World Bank (2011): WDI

Trade openness

357

70.44

36.52

Imports + exports as a % of GDP. World Bank (2011): WDI

Economic integration

328

37.62

14.54

Actual flows of trade and investment and their restrictions, expressed as a % of GDP. Dreher (2006, revised 2011)

Cross border transaction

336

44.48

21.01

Actual flows of trade, FDI +portfolio + payments to foreigners) as a % of GDP. Dreher (2006, revised 2011)

Capital acc. openness

358

-0.77

0.96

Chinn-Ito's de jure index (revised 2011)

Financial openness242

106.2560.42

De facto fin openness. Lane and Milesi-Ferretti (2007, revised 2011).

FDI liabilities

359

3.10

7.49

Share of FDI liabilities in GDP. Lane and Milesi-Ferretti (2007, revised 2011)

Government size

343

15.89

6.78

Government expenditure as % of GDP. World Bank (2011): WDI

Political rights

377

5.14

1.59

The extent of political rights in a country as calculated by Freedom House. Coded from 1-7 (7 being the worst). FH surveys (2011)

Regime type

375

19.65

33.66

Ranges from Monarchy, Military, One-party, Multi-party system to full Democracy (higher value), Teorell and Hadenius (2007).

Reserves

366

5.07

1.88

FX Reserves minus gold (% GDP). Lane and Milesi-Ferretti (2007, revised 2011)

Terms of trade275

113.3841.67

Net barter terms of trade index. World Bank (2011): WDI

Consumption volatility384-64.141242.18

Standard deviation of consumption. Underlying data from PWT 7.0 (2011)

Polity

356

2.96

1.78

Executive Constraints (Decision Rules): from (1) Unlimited Authority to (7) Limited Authority. PolityIV dataset (2011)41

Figure A1: Growth performance around a sudden stop crisis (Guinea-Bissau)

Figure A2: Growth performance around a sovereign debt crisis (Niger)

Figure A3: Growth performance around a currency crisis (Ethiopia)

Figure A4: Growth performance around a twin crisis

42