Download Vulnerability in Small Island Economies. The case of the Caribbean

Vulnerability in Small Island Economies.
The case of the Caribbean
Sebastian Auguste (UTDT) and Magdalena Cornejo (UTDT)
March 2015
Abstract
This paper discusses and proposes a measure of growth vulnerability for Caribbean
economies based on the probability of a growth crisis. The results of our probit estimations
show that in the economies we studied a few variables can successfully predict growth
crises, and probably this is due to the undiversified structure of the economies. More
notably, most of the variables that can trigger a growth crisis are foreign shocks.

Universidad Torcuato Di Tella. [email protected], [email protected]
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1. Introduction
Small Island Developing States (SIDS) are perceived as being more vulnerable to
macroeconomic shocks than the average developing country. Briguglio (2004) states that
many factors such as small size, insularity, remoteness and proneness to natural disasters
render these economies very vulnerable to forces outside their control. Armstrong and Read
(2003), based on empirical evidence, conclude small states are extremely sensitive to the
effects of globalization due to their degree of openness and heavy dependence upon trade
for growth make.
The Caribbean economies we study (Barbados, Belize, Jamaica and Trinidad and Tobago)
are considered SIDS according to UNDP (with the exception of Belize, which is not an
island, but shares most of the other characteristics).1 They fit very well with this general
description of vulnerability and SIDS. Being small, production is usually not very much
diversified, and the economies tend to be more open to international trade; increasing the
exposure to terms of trade shocks or other external real shocks. Some of the countries are
specialized in few commodities, such as Trinidad and Tobago (petroleum and gas) and
Jamaica (bauxite); others are specialized in services, such as tourism (Jamaica) and in a few
cases financial services (Barbados is specialized in tourism and financial services), what
makes these economies sensitivity to developed countries’ business cycles.
Another characteristic of small economies is the diseconomies of scale that face in
production, what particularly affect, among other things, the public good provision. Other
thing equal, the large economies of scale in public services and public good provision
means that SIDS faces more costly services.
These economies are located in natural-disaster-prone zone – disasters such as tropical
storms and hurricanes. These are large scale shocks that the economies are not able to
hedge, destroying capital stock and reducing long term growth. Because most of these
countries are islands, economies are more isolated and vulnerable to climate change.
1
The United Nations Department of Economic and Social Affairs identifies the following 23 countries in the
Caribbean Region as SIDS: Anguilla, Antigua & Barbuda, Aruba, The Bahamas, Barbados, Belize, British
Virgin Islands, Cuba, Dominica, Dominican Republic, Grenada, Guyana, Haiti, Jamaica, Montserrat,
Netherlands Antilles, Puerto Rico, St Kitts and Nevis, St. Lucia, St. Vincent and the Grenadines, Suriname,
Trinidad & Tobago and United Sates Virgin Islands. http://www.un.org/special-rep/ohrlls/sid/list.htm
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In the Caribbean, we have to add as a structural characteristic the fiscal fragility. In most of
the Caribbean economies fiscal imbalances have accumulated in high levels of public debt,
in some cases exceeding its GDP. The fiscal imbalance might be explained both by the
expenditure side (a more expensive public good provision due to lack of economies of scale
and the fiscal cost of natural disasters) and by the revenue side, as these economies have
competed to attract FDI using heavily fiscal incentives (tax competition). Natural disasters
might have also its share in explaining fiscal fragility, as reconstruction efforts imply more
public expenditures.
All these factors affect both macroeconomic volatility and vulnerability, concepts that we
do not treat as synonymous. As Guillaumont (2010) defines, economic vulnerability is the
risk of a (usually poor) country seeing its development hampered by a natural disaster or
external shocks. Volatility, on the other hand, refers to how the economic situation changes
from one period to another; what does not necessary implies a growth crisis.
We understand vulnerability as the probability of having a very bad event, related to the
concept of Value at Risk (VaR) in finance,2 whereas volatility is more related to the
concept of variance (or coefficient of variation) where the risk is being away from the
expected value, not distinguishing between bad and good events (only the deviation
matters). We believe the two economic concepts, vulnerability and volatility, answer
different economic questions, in the same venue VaR and variance measure two different
concepts of risk in finance.
There is a large body of evidence finding that adverse external shocks have a significant
negative impact on growth (Collier and Goderis, 2009; Berg et al., 2010; Aghion et al.,
2010; Bruckner and Ciccone; 2010). Moreover, these effects are asymmetric: while
negative shocks impede growth, positive shocks do not necessarily contribute to long-run
growth (Collier and Goderis, 2007). Of course volatility has a similar correlation with
growth, but as Hnatkovska and Loayza (2004) states, the negative association between
volatility and growth reflects in fact the harmful impact of sharp negative fluctuations
(crisis volatility) rather than the effect of repeated but small cyclical movements (normal
2
VaR is the risk of loss in a specific portfolio, the worst return the portfolio can attain in a given period of
time under normal market conditions, Jorion (1996).
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volatility). This evidence justifies the need to understand better vulnerability, the focus of
the present paper.
In this work we review the literature on measuring vulnerability and we propose a
(regression based) measurement of vulnerability, which we estimate in four Caribbean
countries. Basically we define a binary response variable, being or not in economic crisis,
measured by GDP growth. Next we estimate an M-probit model explaining the probability
of entering in a crisis taking into account the correlation between these economies. The Mprobit specification allows each economy to have different variables affecting the growth
process, as each equation can have different variables. Therefore, by letting the data to tell
us which variables are significant, we include exposure and resilience at the same time.
To explain the growth crisis we explore different variables the literature suggest as relevant
and look for the best specification (in terms of predicted power). The estimations are done
for four countries: Barbados, Belize, Jamaica and Trinidad and Tobago, Caribbean
economies that have quarterly GDP data long enough to do the econometrics. Our results
suggest that: (i) just a few economics variables are necessary to have good predict power
(conditional on previous growth), (ii) crisis are path dependant, the same shock has
different results depending on which part of an economic cycle the economy is in (so
vulnerability is positive correlated with the economic cycle), and (iii) what trigger growth
crisis in all the cases are foreign shocks (including natural disasters); local shocks are not
significant to explain mayor crisis, although by affecting the economic cycle and long term
growth rate, local policy might put the economy at more risk. Our measure of vulnerability,
since it is regression based, has several advantages: (a) it selects variables based on their
predicted power (i.e. it is less arbitrary), (b) it includes resilience, the ability of an economy
to cope with the shock (which is captured in the parameters of the model, which are
different for each country, and ultimately relates the foreign shock with growth), (c) it has
more flexibility in the specification, as we can use different variables for each country, (d)
captures in a more natural way nonlinearities and interactions among variables (because the
model is nonlinear), and (e) it can be used to test parameter stability or policy changes (to
see if resilience improves after a policy change). A limitation of our approach is that we
need long time series and a relatively stable vulnerability structure.
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The remainder of the paper has the following structure. Section 2 presents a literature
review on measuring vulnerability and volatility. Section 3 develops a simple model of
vulnerability based on regression modelling of binary response variables. Section 4
describes the data used in this work and presents some descriptive statistics of the region
and the four economies we analyse: Barbados, Belize, Jamaica and Trinidad and Tobago.
Section 5 presents the main econometric results for the two models we estimate, and finally
Section 6 summarizes the main conclusions.
2. Literature review
Although there is no universal definition of vulnerability, it has been often associated with
the idea of proneness to damage from external factors. The macroeconomic literature has
often treated volatility and vulnerability as synonymous. A good number of recent works
has started to treat the concept of vulnerability in a more consistent way with the
microeconomic literature, as a situation at risk of having a bad event, and not just
volatility.3 In this line, Guillaumont (2009) defines macroeconomic vulnerability as the
likelihood that a country’s development process is hindered by the occurrence of exogenous
unforeseen events or external shocks depending on the size, frequency, exposure to and
capacity to react to shocks.
More generally, economic vulnerability could be approached from the micro or macro
perspective. While the microeconomic perspective is concerned with the impact of shocks
on the well-being of individual households, the macroeconomic perspective focuses more
on the impact of these shocks on macro variables such as economic growth, Seth and Ragab
(2012). In addition it can be distinguished according to what variable we are interested in
measuring the risk: economic vulnerability (growth), environmental vulnerability
(ecosystem) or social vulnerability (poverty or social cohesion).
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Usually the literature distinguished three main kinds of shocks or sources of vulnerability:
(i)
environmental or ‘natural’ shocks (earthquakes, volcanic eruptions, typhoons, hurricanes,
droughts, floods, etc.,)
(ii)
external shocks, related to changes in the international context that affect the economy, such as
changes in commodity prices, changes in the U.S. interest rate, etc.
(iii)
domestic shocks, related to domestic policies, such as fiscal policy, change in rules, unforeseen
political changes, etc.
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In the empirical literature, the economic dimension of vulnerability is the most studied.
This economic approach has either focus on addressing the potential impact of financial
crisis (Kaminsky et al., 1998; Ocampo, 2008), or as structural conditions that expose
economies to economic or financial shocks (Briguglio, 1995, Guillaumont, 2010).
Vulnerability can be seen as the result of three components: (i) the size and frequency of
the shocks (whether the shock happens), (ii) the exposure to shocks (whether the shock hit
the economy); and (iii) the capacity to react to shocks, or resilience (whether the shock that
the economy is exposed makes significant damage). It is believe that public policy has a
major role in resilience. The terms “vulnerability,” “resilience,” and “adaptive capacity” are
usually used with different meanings. As quoted by Malone (2009), perhaps the literature
of climate change has a more elaborated distinction, since the Intergovernmental Panel on
Climate Change (IPCC) defines:
• Vulnerability is the degree to which a system is susceptible to, and unable to cope
with, adverse effects of climate change, including climate variability and extremes.
Vulnerability is a function of the character, magnitude, and rate of climate change
and variation to which a system is exposed, its sensitivity, and its adaptive capacity
(IPCC WG2 2007:883).
• Resilience: The ability of a social or ecological system to absorb disturbances
while retaining the same basic structure and ways of functioning, the capacity for
self-organisation, and the capacity to adapt to stress and change (IPCC WG2 2007:
880).
2.1 Empirical evidence
In the literature we found different attempts to construct a vulnerability index which will
(ideally) predict the timing of such events, identifying the underlying factors that
predispose countries to growth declines. In this section we review the empirical literature
related to the factors determining vulnerability in developing economies, as well as the
different econometric approaches followed to explain economic vulnerability, grouping the
different approaches in three.
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1. Indices
A branch of the literature develops indices based on observable variables, where the main
challenge is how to add up the different variables in just one index, what necessarily needs
to weight the different factors. The weights can be constructed ad hoc (assumed by the
researcher) or using some econometric technique such as principal components. These
indices mostly capture structural conditions of these economies, such as exposure to foreign
economic conditions or proneness to natural disasters. Being structural, these conditions do
not change much across time, and the index is most useful to classify countries according to
their frequency of shocks or exposure more than to predict a bad event. A challenge in this
literature has been how to incorporate to the measure the concept of resilience, which is not
structural but depends more on the policy mix of the economy. Resilience is a very
important concept because it is what finally determines whether the shocks have real effects
on the economy. Making an analogy with medicine, a person might be exposed to an
illness, but if her body has antibodies, it is not vulnerable to the illness. The difficulty with
the add-hoc indexes is to measure the antibodies of the economies.
An early economic vulnerability index (EVI) was prepared by Briguglio (1992) for
UNCTAD which was composed of three variables: exposure to foreign economic
conditions, insularity and remoteness and proneness to natural disasters. United Nations
(1994) also develops an index in the same spirit. Birgulio (1995) expands EVI to five
structural characteristics: economic openness; export concentration; peripherality;
dependence on strategic imports; and dependence on foreign sources of finance. 4 This
index was further developed by Brigulio’s works (Briguglio, 1995; Briguglio, 1997;
Briguglio, 2002; Briguglio and Galea, 2003; Briguglio et al., 2009). The most recent
version of EVI uses the same structural conditions that expose an economy to shocks but
includes economic openness, export concentration and dependence on strategic imports,
Briguglio et al., 2009. Baritto (2008) proposes the Global Vulnerability Index, as an
alternative to EVI, which includes in addition to trade variables: foreign reserve holdings,
share of agriculture on GDP and poverty rate. Liou and Ding (2004) use factor analysis to
construct a vulnerability index from a set of six indicators, namely, domestic economic
4 The most developed version of EVI uses the same structural conditions that expose an economy to shocks
but includes economic openness, export concentration and dependence on strategic imports.
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scale, international trade capacity, development level, degree of output volatility, inflow of
external resources, and institutional capacity.
Easter (1998) compiles a vulnerability index for small states that consists of three
indicators: export dependency ratio, merchandise export diversification and susceptibility to
natural disasters. An Environmental Vulnerability Index (EVI) was developed by the South
Pacific Applied Geoscience Commission (SOPAC) based on a theoretical framework that
identified three aspects of vulnerability: risks to the environment (natural and
anthropogenic), the innate ability of the environment to cope with the risks (resilience) and
ecosystem integrity (the health or condition of the environment as a result of past impacts).
Turvey (2007), focusing only on small island developing states, constructs a composite
vulnerability index (CVI) using four broad groups of indicators: coastal indicators,
peripherality indicators, urbanization indicators, and indicators of the vulnerability to
natural disasters.
For the Caribbean region, Crowards (2000) proposed an index of economic vulnerability
that suggests a negative non-linear relationship between economic vulnerability and
country size. A Social Vulnerability Index (SVI) was developed by St. Bernard (2007). He
defines social vulnerability as the inability of human units (individuals, households or
families) to cope with and recover from stresses and shocks, their inability to adopt and
exploit changes in physical, social and economic environments and their inability to
maintain and enhance future generations.
Table 1 summarizes the indicators and aggregation methodology used by different
vulnerability indices. These simple ad-hoc indices have the advantage of allowing the
comparison across countries very easily, as it uses the same variables for every country.
Therefore, they are good to identify which country is exposed more to a shock, but
necessary is good to predict a crisis. In particular, as the index do not change in time, it
cannot show when the country is more vulnerable (more likely to suffer a growth crisis).
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Table 1. Vulnerability Indices
Vulnerability
Index
Economic
Vulnerability
Index (EcVI)
Description
Indicators and aggregation
Briguglio
(1995),
Briguglio
(1997)
The construction of a composite index of vulnerability is
intended as a measurement of the lack of economic
resilience arising from the relative inability of a small
island state to shelter itself from forces outside its
control.
Economic
Vulnerability
Index (EVI)
United Nations
Committee for
Development
Policy
Commonwealth
Vulnerability
Index
Commonwealth
Secretariat Atkins, Mazzi
and Easter
(2000)
Crowards
(2000)
The EVI is designed to reflect the risk posed to a
country's development by exogenous shocks, the impact
of which depends on the magnitude of the shocks and on
structural characteristics that determine the extent to
which the country would be affected by such shocks
(resilience).
The Commonwealth vulnerability index is designed to
quantify vulnerability, and hence provide a means to
identify vulnerable states
1. Exposure to foreign economic conditions (ratio of exports and imports to GDP)
2. Remoteness and insularity (ratio of transport and freight costs to exports proceeds)
3. Disaster proneness (money damage in relation to GDP
A standardization procedure is performed to render the index insensitive to the scale of measurement.
Two set of weights are considered: an equally weighted index and 50% to economic exposure, 40% to
transport and 10% to disaster-proneness.
Eight indicators grouped into two broad areas:

Exposure index (1/2): Population (1/8), Remoteness (1/8), Merchandise export concentration
(1/16), Share of agriculture, forestry and fisheries (1/16), Share of population in low elevated
coastal zones (1/8),
1. Shock index (1/2): Instability of exports of goods and services (1/4), Victims of natural
disasters (1/8), Instability of agricultural production (1/8)
Economic variables: Openness degree, Inflation, Food import, Total debt service
Environmental variables: Natural disasters victims
Insularity variables: Logistics performance index, Population size
The weights attached to each indicator to aggregate to a single index are determined directly from an
estimation procedure, being the estimated coefficients of the model.
Peripherality or accessibility, Dependence upon imported energy, Export concentration , Convergence of
export destination, Reliance upon external finance or capital, Susceptibility to natural disasters
Economic
Vulnerability
Index
Social
Vulnerability
Index (for the
Caribbean) SVI
Environmental
Vulnerability
Index (EVI)
Author
St. Bernard
(2007)
South Pacific
Applied
Geoscience
Commission
(SOPAC)
The proposed index suggests a negative non-linear
relationship between economic vulnerability index and
country size, as measured by total population.
Social vulnerability is the inability of human units
(individuals, households or families to cope with and
recover from stresses and shocks, their inability to adopt
to and exploit changes in physical, social and economic
environments and their inability to maintain and
enhance future generations.
It is based on a theoretical framework that identified
three aspects of vulnerability: risks to the environment
(natural and anthropogenic), the innate ability of the
environment to cope with the risks (resilience) and
ecosystem integrity (the health or condition of the
environment as a result of past impacts).
The index comprises 13 indicators in the following five main domains:
Health: Life expectancy at birth
Security: (i) Index of rule of law, (ii) Measure of minority groups’ participation in the economy,
Measure of new/present government’s respect for the commitments of previous governments, (iii)
Indictable crimes per population
Resource Allocation: (i) Proportion of children under 15 belonging to the two poorest quintiles, (ii)
Proportion of the population 15-64 belonging to the two poorest quintiles, (iii) Proportion of the
population 15-64 belonging to the two poorest quintiles which have no medical insurance, (iv) Proportion
of the population belonging to the two poorest quintiles in which the head is unemployed.
Education: (i) The proportion of the population 20 years and over with exposure to tertiary level
education, (ii) The proportion of the population 20 years and over that has successfully completed
secondary education with a minimum of 5 GCE/CXC passes or equivalent secondary school leaving
qualifications, (iii) Adult literacy rate of population aged 15 years and over.
Communication Architecture: (i) Computer literacy rate of population aged 15 years and over.
St. Bernard propose a linear combination of the indicators, which are equally weighted
It is composed by three sub-indices:

REI (Risk Exposure Index): composed by 39 indicators

IRI (Intrinsic Resiliency Index): composed by 5 indicators

EDI (Environmental Degradation Index): composed by 13 indicators.
The EVI is calculated as a weighted average of scores allocated in the range 0-7 derived from a total of 57
indicators.
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In addition, a problem with simple indices is that they do not capture well the different
concepts behind vulnerability. Mostly they only measure exposure to the shock, but not the
resilience. If we want to predict a growth crisis for an individual country the index has less
sense as these are structural variables that do not change much through time. When
vulnerability is seen as growth crisis, there are three concepts that should be taken into
account: (i) the shock, (ii) exposure to the shock, and (iii) resilience (the country’s coping
capacities to deal with the impact of the shock.
Briguglio et al. (2009) discuss this and develops different ad-hoc indices to capture the
different concepts, as shown in Figure 1. But the way the index is constructed again is more
helpful to understand the relative position of an economy than predicting a crisis, as the
factors behind resilience are also structural and do not change much over time. In addition,
it does not have flexibility to capture the specificities of each economy, as all have the same
variables. For instance, good governance can impact resilience in different ways and
countries exposed to different kind of shocks can show different factors of resilience. In
other words, what can be good to increase resilience in one country exposed to a particular
type of shock could be bad for other country with a different exposure.
Figure 1. Vulnerability and Resilience
Source: Birguglio et al. (2009)
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Adding up different factors in a unique index is ad hoc and controversial. Techniques such
as principal components or factor analysis should be better. It intends to capture the
common source of variation across the factors. The problem is that a country could be
vulnerable to very different kind of shocks, such as natural disasters or a commodity price
crisis, and they might not have something in common to extract.
The simple index by trying to use the same variables and the same weights for all the
economies is oversimplifying the complexity of vulnerability.
2. Regression-based indices
The vulnerability index is a composite index which weights and averages a given number
of component indicators. The selected indicators must reflect the exogenous economic
forces generating economic vulnerability. The construction of this index is indeed arbitrary
as the weights and results depend on the number and choice of components. To avoid such
arbitrariness, some studies choose a set of component indicators previously selected from
an econometric regression on economic growth (e.g. Atkins et al., 2000; Guillaumont and
Chauvet, 2001; Peretz et al., 2001). Therefore, this methodology follows a two-stage
procedure. First, an econometric model helps determining which variables explain
vulnerability. Second, the model so developed is used to predict individual vulnerability
scores which will form the composite index. Atkins et al. (2000), for instance, develops the
Commonwealth Vulnerability Index, which comprises seven indicators covering economic,
environmental and insularity dimensions. The weights attached to each indicator to
aggregate to a single index are determined directly from an estimation procedure, being the
estimated coefficients of the model.
A simple way to incorporate both aspects in a single measure is through a Probit model, in
the spirit of the literature on early warning system models. Three papers using this
approach are Easterly and Kraay (2000), IMF (2011) and Dabla-Norris and Gündüz (2014).
Easterly and Kraay (2000) estimate a panel probit regression relating growth downturns
(negative growth rates) to a large set of structural variables in developing and advanced
economies. Once the probit model is estimated, the same model can be used to predict
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future crisis. Dabla-Norris and Gündüz (2014) develop a vulnerability index which
provides warning signals of a growth crisis in low-income countries, selecting some
macroeconomic and institutional indicators by conducting a panel probit regression. Their
results show that country fundamentals, exchange rate regimes, institutional quality and the
size of shocks are important determinants of growth crises in low income countries. In this
line, Méndez Quesada and Solera Ramírez (2004) found that the variables with significant
effect on the probability of a growth crisis in Costa Rica were the ratio of M2 to net
international reserves, the exchange rates changes, the oil price and the interest rate
differentials.
This Probit/Logit model has been largely used in the literature on Early Warning Systems
(EWS) which attempts to anticipate future crises (Frankel and Rose, 1996; Bussière and
Fratzscher, 2006), but it has not been the only methodological approach in this literature,
being the “signal extraction model” an equally used alternative (Kaminsky et al., 1998).
The EWS approach consist in the analysis of the economic and financial indicators which
enable the acquisition of information related to a potential vulnerability of the balance of
payments or the lack of sustainability of the exchange rate. See Percic et al. (2013) for a
recent review of this literature. The method based on signal extraction is a non-parametric
approach which is usually univariate (criticisms are that it does not allow testing of the
statistical significance level of the variables).
Finally, another methodology extensively used in the empirical literature is the MarkovSwitching (MS) model of Hamilton (1989), also known as the regime switching model. A
MS model, in the context of predicting an economic crisis, involves using structures
(equations) that can characterize the GDP behavior in two different regimes: boom or
recession. As the state variables (the prevailing regime at time t) are not directly observed,
they are assumed to follow a particular stochastic process (a 2-state Markov chain, in this
particular case). Therefore, the transition matrix defines the transition probabilities that
will govern the random behavior of the state variables. An advantage of such model is that
the regime classification is probabilistic and determined by data. If the regime equation is a
constant (i.e. when the intention is not to explain the level of the GDP in each regime, and
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what matters is if the economy is or not in a crisis mode) the switching regression model
simplifies to a simple probit or logit model.
Finally, recent work has developed a new and promising framework that allows to predict
low frequency data (such as GDP growth) with high frequency data (such as daily price
movement in the stock market). In this line, we can mention the MIDAS (MIxed DAta
Sampling), which was first introduced by Ghysels et al. (2005). The original work on
MIDAS focused on volatility predictions (e.g. Beker et al., 2007; Clements et al., 2008;
Chen and Ghysels, 2011; among others). Following Andreou et al. (2011), a MIDAS
volatility regression model with high-frequency is given by,
k max
Vt (m1,)t
     w(k , ) X tDk   t
k 0
where Vt (m1,)t is a volatility measure in the next period at some frequency m (e.g. monthly)
and X tD k are the forecasting variables available at daily or greater frequencies. The term
k max
 w(k , ) X
k 0
D
t k
is a parsimonious and data-driven aggregation scheme based on a low-
dimensional high-frequency lag polynomial. There are various alternatives for the
polynomial specification like the exponential Almon lag or the Beta lag. This specification
provides a method to investigate whether the use of high-frequency data necessarily leads
to better volatility forecasts at various horizons. Furthermore, the MIDAS regressions do
not exploit an autoregressive scheme (like the ARCH approach), so that X tD k is not
necessarily related to lags of the volatility variable. An alternative and useful way of
handling mixed frequency data is the Nowcasting approach. Nowcasting models use a wide
variety of indicators and their bivariate relationship with the dependent variable to predict
within a period (e.g. quarter). By relying on bridge equations which are specified at a
higher frequency than the variable to be nowcasted, the individual-indicator forecasts are
then aggregated using different weighting criteria to obtain an overall forecast of the
variable of interest.
3. Volatility analysis
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Finally there is large literature on macroeconomic forecasting and volatility, such as the
VAR and GARCH (Generalized Autoregressive Conditional Heteroskedasticity) models,
widely used to model time-variation in conditional variance. The GARCH model (and its
extensions5), proposed by Bollerslev (1986), allows describing volatility as an exact
function of a given set of variables. A GARCH(p,q) model is given by,
 t2     ( L) t21   ( L)t2
where  t2 is the conditional variance, t2 are the squared residuals,  ( L) is a polynomial
lag operator of order p and  ( L) is a polynomial lag operator of order q. GARCH models
describe the time evolution of the average size of squared errors, and proved to be
successful in predicting volatility changes. GARCH models focus more on volatility than
on vulnerability, but once the variance in modeled and estimated, it can be used in a sort of
Value at Risk model to understand better what are the effect of the negative shocks or the
probability of entering into a recession.
3. An econometric approach for the Caribbean
As discussed previously there are several alternatives to construct a vulnerability index. In
our work we focus on economic vulnerability, and particularly in economic growth
measured by GDP. We are interested in having an index that can predict for a particular
economy, given its exposure and resilience, the probability of entering in a growth crisis.
The simple ad hoc indices or the indices that select variables based on regressions (the twotier approach) have the problem of not incorporating resilience. We are not interesting in
knowing what particular shocks the economy is exposed to, but if this shocks can really
trigger a crisis. In addition we want to have flexibility in the model formulation, having
different variables for different economies depending on their relevance.
5
Some extensions of the standard GARCH model are: the Exponential GARCH (EGARCH), the threshold
GARCH (TGARCH), the Beta-t-EGARCH, among others.
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A probit model has the advantage of incorporating exposure and coping ability without the
need of modeling it, but letting the data to define which variables are relevant for this
particular country. Structural factors that do not change much across time are captured in
the intercept. The rest of the variables are incorporated depending on whether they have
indeed affected growth at some point in the past. In addition, since it is not linear, the
marginal effect of a variable is conditional on the situation of the other variables, what
makes sense. This is an advantage compared to lineal models where the effect is linear
unless the interaction is modelled, what creates an extra burden in the search for
specification. This problem also appears in the econometric linear models such as OLS or
MIDAS, where nonlinearities are not easily captured and have to be modelled as interacting
variables.
The probit model has the additional advantage of modelling implicitly resilience, which
might be captured in the parameters of the model or some variables included. For instance,
an increase in WTI price might be an external negative shock for a country, but the
estimated coefficient will tell us how this particular shock affects the probability of having
an economic crisis, which can change through time. Parameter stability can be studied to
learn whether the country is improving is resilience to shocks. In other words, our model
predict effective vulnerability, in the sense of what really can the shock do.
The disadvantage of the probit approach to measure vulnerability is precisely the trade-off
between the time window needed to estimate the parameters consistently and the stability
of the parameters. In a simple ad-hoc index, the past does not matter, as the index is
constructed with the value of the variables today with some ad hock weight. If the weights
are based on past data, as in the two tier approach, then the problem of the time windows
arises like in the probit model. The trade-off is between consistency in the estimators
(which ideally needs a long time series) and parameter stability (affected by changes in
economic policies that affect resilience). A shortcut is to use high frequency data, such as
monthly data or quarterly data, but this kind of data is scarce in developing countries.
VAR and GARCH models are not appropriate for our research questions as they focused
more on volatility than vulnerability. Nonlinearity (interaction between variables) is still a
specification problem in these models that should be resolved explicitly.
15
MIDAS approach has the advantage of combining low and high frequency data. Usually
growth is measured annually or quarterly, but it is a continuous process; whereas the
variables to include as regressors, such as WTI price, are measured in high frequency (daily
or monthly). If the WTI price increases today, I would like to know today what its effect on
growth is, and see how the daily movements (high frequency data) affect the growth
process (low frequency data). This is attractive for our case, but MIDAS approach has a
limitation. Basically it runs individual regressions of Y on individual X, modelling the high
to low frequency relationship using a mixed model, and then the individual models are
added up to a single parameter (i.e. it is not a multivariate regression but rather an average
of multiple univariate regressions). The aggregation problem is similar to the one presented
by the simple index or principal-component-based indices (in our study case there are
multiple and completely differently causes of growth crisis).
This discussion leads us to choose a Probit approach for our paper, in order to assess the
probability of an economic crisis or entering a period of no-growth. We define a growth
crisis as a dummy variable that takes 1 when the following two conditions hold (similar to
Dabla-Norris and Gündüz, 2014):
(1)
the post-shock two-period average level of real GDP falls below the pre-shock
three-period trend
(2)
the annual growth of real GDP is below the long-run population growth at time t (so
per capita GDP growth is negative). Note that by taking annual growth, we eliminate the
problems of adjusting to seasonability.
All other episodes that do not satisfy both conditions are considered normal episodes and
take on a value 0. This definition of a negative shock is add-hoc and can be modified not
affecting the econometric implications of our paper. If a less restrictive approach is used
(such as just one period fall in GDP) we will have more negative growth episodes. This is
analogous to a Value at Risk model, that can be estimated with different confidence, such
as 99%, 95% or 90%.
Our M-equation multivariate probit model (in our case, M=4 countries) is:
16
*
yim
  m ' X im   im ,
m  1,..., M
yim  1 if y  0 and 0 otherwise
*
im
where y is the observed outcome: the dummy variable identifying bad events, x is a vector
of potential factors explaining economic vulnerability,  is a vector of the associated
coefficients,  im , m=1,…,M are error terms distributed as multivariate normal, each with
mean zero, and variance-covariance matrix V, where V has values of 1 on the leading
diagonal and correlations  jk  kj as off-diagonal elements.
The advantage of using a M-equation multivariate probit model is that it allows for a
flexible correlation structure over time. If there is correlation, the M-equation probit
estimates the accurate standard errors and the significance of the explanatory variables in
the individual model may change. In addition each equation can have different regressors.
Once the model has been estimated, it can be used to make predictions. If the value of
predicted regressors is x0 the probability of entering in a growth crisis episode is given by:
𝑃(𝑦 ̂
= 1/x0 ) = (x´0 𝛽̂ )
where,  is the cumulative normal density function.
The predicted probability is our vulnerability index, that tells us which is the probability for
a particular country of entering in a growth crisis conditional on the state of the word (x0)
and the resilience of this country ().
A change in one particular variable xi has a nonlinear effect on the probability of a growth
crisis:
𝜕𝑃(𝑦𝑡 = 1/x𝑡 )
= 𝛽𝑖 𝜑(x´𝑡 β)
𝜕𝑥𝑖
which depends on the parameter i as well as on the other variables and estimated betas,
through the normal density function 𝜑. This marginal effect allows interaction between all
the variables, what is essential in vulnerability analysis.
17
The constant of this model captures all the variables (and its associate resilience) that do
not change across time. If all the x´s are zero, the constant plugged into the model will give
us a time invariant probability of entering into a crisis. The x variables, therefore, captures
the factors that move this economy up or down across time in terms of the probability. If no
variable is significant, it does not necessarily mean the model predicts badly; it could be
possible that vulnerability is time invariant. If local variables (such as fiscal deficit) are not
significant in the model, it does not mean local factors do not matter, but rather they have
not the ability to increase or decrease the vulnerability across time.
In our work, instead of using a panel, which forces the same x variables and  for all
countries, we estimate individual probit models for each country but taking into account
that error terms might be correlated across economies. In particular we estimate the Mequation multivariate probit regression using simulated maximum likelihood (see
Cappellari and Jenkins, 2003), that allows a flexible variance specification for the error
term.
By running country level regression we have more flexibility to use different variables or
specifications that adjust better to each particular country and we better capture
idiosyncratic risks (models fit better in individual time series than in panel data). In
addition, as the model allows different  for each country, it captures differences in
resilience.
3.1. Data description
The main variable of interest is quarterly real GDP, obtained from the Caribbean Centre for
Money and Finance (CCMF).6 This variable is available for the countries we study from
2000 to 2012 (other Caribbean countries have shorter time seires). The dependent variable,
the growth crisis event (y), is a dummy variable that equals to one when: (a) the annual
growth of real GDP is below the long-run population growth at time t, and (b) the postshock two-period average level of real GDP falls below the pre-shock three-period trend.
Annual population growth rate was obtained from the World Development Indicators
database (World Bank).
6
http://ccmf-uwi.org/
18
As for the controls, we propose several variables according to potential explanatory
variables discussed in Section 4 and look for the most parsimonious model. In particular,
we consider the effects of natural disasters (storm, hurricanes and floods, among others);
external shocks such as slumps in external demand (uisng the US economic growth as a
proxy), world commodity prices instability (mainly oil price), international fluctuations of
interest rates, trade- and exchange-related shocks; and domestic shocks that may not be
quite exogenous, but conditions the structural economic vulnerability (debt-to- GDP ratio,
fiscal deficit-to- GDP ratio, international reserves-to-GDP, among others).7 All the
variables used are quarterly data. Table 2 shows the main controls used in the regressions,
all of them measured quarterly. In addition to the level, we test the model with different
specifications, including lags and (annually and quarterly) growth rates.
Table 2. Data definitions and sources
Variable
Commodity prices
Description
Real prices of oil (WTI), banana, sugar in U.S. dollars
Source
Pink Sheet – World Bank
Natural disasters
Number of Caribbean natural disasters
EM-DAT
Damage
Damage of natural disasters, in 000 U.S. dollars
EM-DAT
U.S. GDP growth
U.S. real gross domestic output growth
Intl. interest rate
10-Year Treasury constant maturities
Exchange rate
Exchange rate, national currency per U.S. dollar
Visitors
Number of tourist visitors
Length stay
Average length stay of tourist visitors, days
TOT
Terms of trade
Debt/GDP
Debt to GDP ratio
Central Banks
Ext Debt/GDP
External debt to GDP ratio
Central Banks
Govt Exp/GDP
Government expenditure to GDP ratio
Central Banks
Fiscal Deficit/GDP
Fiscal deficit to GDP ratio
Central Banks
Intl. Reserves
International reserves
IMF / Bank of Jamaica
Inflation
Annual inflation rate
IMF / Bank of Jamaica
IMF
Federal Reserve
IMF / Bank of Jamaica
Bank of Jamaica / Central
Bank of Barbados
Bank of Jamaica
IMF / Central Bank of
Trinidad and Tobago
7
A study of the Caribbean Group for Cooperation in Economic Development (CGOED, 2002) reveals that the
main determinants of volatility in the Caribbean are natural shocks and disasters, terms of trade disturbances,
macroeconomic policy volatility (such as reactive fiscal or monetary policies), and the domestic and
international financial markets.
19
4. Volatility and vulnerability in the Caribbean
In this section we present a brief description of the economies selected, so the reader can
better understand what the potential shocks that might hurt these economies are, and some
summary statistics of the recent growth process of these countries compared to other SIDS.
The four economies we analyze have many things in common but enough differences to
learn from each experience. They share a common colonial history, speak the same
language, have the same common law approach, are part of the CARICOM, share similar
tropical climate (although Jamaica is clearly the country with more economic costs due to
natural disasters), and are relatively young independent nations (Barbados became
independent in 1966, Belize in 1981, Jamaica in 1962, and Trinidad in Tobago in 1962). In
spite of this apparently similar history, recent economic development shows different
economic patterns. They differ in size, wealth, product space and the reaction to foreign
shocks. Trinidad and Tobago is specialized in energy (oil and gas), Barbados in services
(tourism and financial services), Jamaica in tourism and minerals (bauxite) and Belize (the
only continental country of the four) in agriculture and recently in petroleum (after the
discovery of reservoirs in 2005).
Since independence, of the four countries Jamaica is the one that shows the weakest
economic performance. Its GDP per capita has been growing very modestly, just 0.6% per
year between 1966 and 2012, with numerous economic crises. Not only Jamaica has grown
at a slower pace, but also is the country with the highest volatility (as measured by the
coefficient of variation of the growth rate). The swings in the growth rate are extreme. For
instance, in 1970 the economy grew at almost 11% per capita, but the following year just
1%, and then again at the stunning rate of 16.2% in 1972 to contract 7% in 1973 (and the
rest of the 1970s). Jamaica is the most vulnerable of the four economies according to any of
the vulnerability indexes available internationally. In our empirical work we find Jamaica is
in growth collapse mode 14 out of 48 quarters, which is by far the highest of the four,
followed by Barbados with 11, Trinidad and Tobago with 9 and Belize with just 3.
20
Jamaican economy heavily depends on bauxite/alumina exports as well as on the tourism
industry and remittance inflows, which together accounts for over 85% of foreign
exchange. Tourism accounts for 25% of GDP and 7.2% of direct employment (or 24% of
employment taking into account indirect effects). From the four economies, it is the most
vulnerable to natural disasters (mostly hurricanes). In the last decade, Jamaica suffered two
mayor hurricanes: Ivan (September 2004) and Sandy (October 2013).
Belize in particular has been the most stable economy in the time period we analyse,
favoured by the discoveries in the petroleum industries in 2005 that pushed the economy to
a new level of long run growth. Currently petroleum is the single product ranking number
one in terms of exports (accounting for 29% of the export of goods), but looking at the
economic sector, Belize is still a food exporting country; agriculture (sugar and tropical
fruits) and fishing account for almost 70% of the exports and 30% of the employment.
Tourism has started in Belize more recently than in other Caribbean destinations but it has
been growing fast, being an important source of employment, but it not relatively large as
to influence the volatility of the economy.
Of the four economies, Barbados fits better the stereotype of a sun-and-beach tourism
oriented Caribbean island. With just 280 thousand inhabitants is the smallest country of the
group (close to Belize, 324 thousand, but far from Jamaica, 2.7 million, and T&T, 1.3
million). Tourism has been historically an important source of economic growth, but it is
also true that the country has been able to find new activities (business services) to
diversify the economy in spite of its small size. Today Barbados is the richest economy of
the four, with a GDP per capita in current US dollars close to Trinidad and Tobago (14,917
and 17,437 US dollar respectively), but far from Jamaica and Belize (US$ 5,440 and 4,721
respectively). Its economy is service oriented, with financial services and tourism being the
two most important sectors. Looking at a long time series, it is the least volatile economy of
the four we analyse in terms of GDP growth, although has recently revealed some problems
to ignite growth, affected by the 2001 terrorist attack in the U.S. (which reduced tourism)
and the financial crisis of 2008.
21
Trinidad and Tobago, the largest economy in the Caribbean with 1.36 million people, owns
its relative success to the development of the oil and gas industries, which accounts for
almost 50% of GDP, 80% of exports, but only 5% of employment. The discoveries of
natural gas reservoirs in the 1990s changed the economy from a petroleum based economy
to a mostly natural gas based economy (currently it is a world leading exporter of ammonia
and methanol, and the main supplier of LNG to the U.S. market), but still dependent on
petroleum industries. Tourism has not played an important role in Trinidad, and only in
Tobago has recently started to develop, but still it is very incipient.
In Trinidad and Tobago managing volatility means been able to cope with the ups and
down of the international prices of the petroleum industries products. The country has
improved significantly in the last 40 years with this regard. In the 1970s, the government
policy exacerbated the cycle of the economy induced by petroleum price, what was very
costly for the economy in the 1980s, recording 10 consecutive years with GDP contraction
(in the lost decade, the economy contracted 34% between 1982 and 1992, and it only
recovered the pre-crisis GDP in 2002, 20 years later. As Artana, Bour and Navajas (2006)
point out, overall fiscal policy was expansive in the boom; government expenditures were
not carefully evaluated so as to ensure positive social rates of return, government subsidies
eased private investment in declining industries, and abundant capital inflows were spent.
The strong appreciation of the real exchange rate (RER) generated Dutch disease problems,
affecting the nonpetroleum industries. When prices of crude oil dropped in international
markets, the country used its foreign reserves (which fell from US$3.3 billion in 1981 to
US$0.2 billion in 1992); even so, it could not avoid recession, and unemployment soared.
In the recent boom of the 2000s the policies in Trinidad and Tobago have been more
prudent, increasing resilience. An important development has been the creation of a
stabilization fund (The Heritage and Stabilization Fund), with the objective of saving a
share of the windfall (preventing the real exchange rate appreciation and having space to do
counter-cyclical spending). At the end of 2013, the net asset value of the HSF is estimated
in US$5.2 billion, around 22% of the GDP. Since 2000 it has gone only through a mayor
crisis, which was the 2008 crisis, but after the crisis has shown some problems to recover
high growth rates.
22
In terms of policy design to build resilience, the four economies have chosen different
models. Trinidad and Tobago and Jamaica have a managed floating exchange rate system,
and have tried to use monetary policy to cope with foreign shocks. Trinidad and Tobago
arguably did a better job, but both Central Banks have shown fear to float in their policies,
and have avoided large depreciations or appreciation, what raises the question of whether
monetary policy has been effectively used to cope with external shocks or it has been rather
endogenous.
Barbados and Belize, on the other hand, have a strong peg to the US dollar, and the
monetary policy to cope with shocks is restricted to the management of domestic interest
rates and reserve requirements that affect the local multiplier. The evidence shows that
these instruments have not been used aggressively to have an accommodative monetary
policy, and monetary policy has been rather passive.
Either by fear of appreciation/depreciation or by the strong peg, monetary policy has not
been used aggressively enough to cope with foreign shocks and its main objective has been
stabilizing prices, goal that have been able to fulfil with some success. What has been the
role of the fiscal policy? Trinidad and Tobago has US$ 5.2 billion, around 22% of the GDP,
saved in the stabilization fund; but the other three countries face a common limitation to
use fiscal policy to accommodate shocks: the very high levels of public debt. Jamaica is the
most indebt country in the Caribbean (140% GDP) followed by Barbados (83%), and
Belize (76.8%). With high debt, the eventual benefits of expanding public expenditure in
crisis should be trade off with the risks and financial costs of increasing debt in already
high debt countries. Caribbean countries have been trying to reduce the debt burden, but
they have a structural deficit problem that have proved difficult to resolve in hard to tax
economies (open and small economies that have used tax competition to attract FDI).
Recently the debate has been more on the direction of fiscal consolidation to solve fiscal
deficit and debt problems that in using fiscal policy countercyclical.
The Barbados experience is interesting, as it has developed a fiscal reaction mechanism,
which according to Worrell et al (2003, 2006) has been essential for building resilience in
spite of the monetary rigidity. This fiscal reaction has allowed in the past to sustain the hard
23
peg. An example of this reaction was the last mayor crisis of 1991, when the government
corrected a 7 percent of GDP fiscal deficit by cutting 8% public sector salaries, 10% public
employment, and 50% government capital outlays, at the same time that privatized some
public companies and increased taxes, Worrel et al (2003). Barbados with this sharp fiscal
contraction was able to reduce the fiscal deficit to 1.6 percent of the GDP in just one year
As Worrel et al (2006) discuss, this sharp adjustments requires governance conditions that
are rather unusual in developing countries. We argue in this paper, that Barbados fiscal
policy response seems to be a last resource instrument, used mainly in crisis period. In
recent years the country has not been able to prevent fiscal deficits and debt accumulation.
The public debt-to-GDP ratio rose to from 56% in 2008 to 83% in 2012, and only when the
crisis is imminent, the drastic fiscal consolidation program emerges, as it happened recently
with the 2013 policy package announced by the government. It is true that such drastic
policies requires a lot of coordination, and public opinion cohesion is probably more likely
to happen under extreme events, like a currency crisis or a natural disaster, but not in small
events (what might explain Barbados behaviour).
Public policy and resilience
The small size of the economy limits the type of industries that can be efficiently
developed, unless they are export-oriented (market size matters). To be worldwide
competitive the economies should limit its product space to fewer products. But given the
high transportation costs, and the lack of local inputs, not any industry can be efficiently
developed (even when it is export-oriented). These structural conditions called for open
economies, which should be very efficient (and ideally focused on high value added goods
per unit of transportation) to be internationally competitive. The lack of economic
diversification implies the economies are vulnerable to the international demand conditions
of their main exportable good or services as well as the price of the main imports (i.e. they
tend to suffer more volatility in the terms of trade, Easterly and Kraay (2000)).
In addition, Caribbean states are vulnerable to natural disasters (tropical storms and
hurricanes), what might generate significant economic cost by destroying capital stock, and
unbalancing the fiscal and external accounts (as the reconstruction increase the demand for
imports). Given this characteristics, it is not surprising that SIDS are characterised by high
24
investment ratios and a high dependence of foreign savings, see Birchwood and Brackin
(2009).
Trinidad and Tobago are the southernmost of the Caribbean islands and are located just
seven miles off Venezuela’s north-eastern coast. Because of its location, is less vulnerable
to natural disasters such as hurricanes and tropical storms than other Caribbean countries.
Independently of the development strategy followed after independence, the Caribbean
economies are characterized by strong States, in many cases with public participation in the
private provision of good and services (more common in the past that in the present), but
relatively low taxation, particularly corporate taxation. The need for attracting foreign
direct investment to develop their industries has pushed these states to give fiscal incentives
that erode their fiscal basis. Some states like Barbados are recognized as a low taxation
economy, providing important benefits to FDI in a relatively neutral way, whereas others,
like Jamaica, have used the fiscal benefits in a much more arbitrary way, making the
process less transparent and distortionary.
Low fiscal revenues contravene the more costly public good provision in SIDS (due to the
lack of economies of scale and isolation). In addition, fiscal revenues are more volatile as
they are based on foreign trade in countries that are in more desperate need of
countercyclical policies. These characteristic, when added to the previous description,
creates an unhealthy combination: Caribbean countries easily fall in fiscal deficit and public
debt accumulation.
Caribbean countries rank in the top 30 of the world’s highly indebted emerging market
countries (e.g. Jamaica has a public debt of 150% of GDP). Sahay (2005) shows that most
of the increase in their public debt is accounted for by a deterioration in primary fiscal
balances that has been largely due to a sharp increase in expenditures rather than a fall in
revenues: the rapid increase in fiscal expansion was related to policy slippages, insufficient
fiscal planning for anticipated adverse shocks, and, to some extent, unanticipated shocks.
25
It is also true that given their small size, SIDS by nature are at a disadvantage in generating
sizable and stable revenue bases as they possess lower populations, smaller markets and a
narrow base for revenue generation relative to industrialised economies, see IMF (2010).
Fiscal revenues in many SIDS countries have relied on foreign trade, what makes fiscal
revenues more volatile, linked to the volatility of trade in goods and services. The link
between external and fiscal balances in the Caribbean region was recognised by Birchwood
and Mathias (2007) and Alleyne et al (2011). They showed that the external trade balance
had a positive effect on the fiscal balance. Ehrhart and Guerineau (2012) show that the
relationship between trade and taxes is more general: they showed that tax revenues of
developing countries tended to increase with rising commodity prices, linking fiscal
revenue volatility to commodity price volatilities.
As a result, Caribbean economies are characterized by fiscal deficit and high debt, implying
a tight fiscal space, constraining their ability to address recurrent and developmental
challenges.8
On the monetary side of the stabilization problem, most of the Caribbean economies do not
actively use independent monetary policies, and they are dominated mostly by the
monetary policy of the U.S. Most of the countries in the region follow a fixed exchange rate
regime (some of them using the hardest possible fixed regime through dollarization) and a
few are currently using managed floating rates, Birchwood (2011).
In the Caribbean, monetary policy has been successful to keep inflation low, what evidently
has been the main objective of Central Banks, and perhaps justify the fear of floating
observed. The question in the Caribbean is whether this monetary approach (pegging to the
US dollar) has been helpful to cope with external shocks, more than whether it was
successful to prevent inflation. As we show in this work, just looking at the exchange rate
regime is not enough to establish the virtuousness of the stabilization policy framework.
8
Heller (2005) defines fiscal space as “the availability of budgetary room that allows a government to
provide resources for a desired purpose without any prejudice to the sustainability of a government’s
financial position.” Aizenman and Jinjarak (2010) had a useful though limited working definition as they
defined de facto fiscal space as “the inverse of the tax-years it would take to repay the public debt.”
26
What is important is consistency between fiscal and monetary policy, something that
Trinidad and Tobago has not shown in the past, but it has learnt to do, and something
Jamaica has struggled to developed. Local underlying vulnerabilities can amplify the
impact of external shocks, and limit their capacity to absorb and mitigate their impact.
Fiscal and monetary responses are the two main pieces that build in the policy framework
to cope with external shocks. They are not independent but rather interact, so they need to
be taken into account jointly, and give the economy enough flexibility to accommodate the
external shocks.
Table 3 summarizes some structural characteristics of these economies affecting public
policy and resilience.
Table 5. The Global Competitiveness Index 2013-2014 rankings (out of 148 countries)
Barbados
General
Institutions
Transparency of government policymaking
Wastefulness of government spending
Macroeconomic Environment
Government Budget Balance
General Government Debt, % GDP
Financial Market Development
Soundness of Banks
Legal Right Index
Trade Tariffs, % duty
47
30
28
28
121
129
121
28
11
12
146
Jamaica
94
85
99
117
141
96
146
47
50
28
90
Trinidad and
Tobago
92
94
94
96
50
51
68
55
37
12
111
Source: author’s estimations based on WEO data.
Recent Economic Growth and Volatility
Tables 4 and 5 summarize some descriptive statistics of the growth process in these
economies. They also show the correlation matrix of the growth rates, which are
surprisingly low.
Table 4. Economic growth
Barbados
Belize
Jamaica
T&T
GDP per capita, current USD
Annual
1960
2012
growth
379 14,917
7.3%
305
4,721
5.4%
429
5,440
5.0%
631 17,437
6.6%
GDP per capita, constant
international 2005 USD
Annual
1960
2012
growth
4,895
14,350
2.1%
968
4,248
2.9%
n.a.
n.a.
4,453
14,183
2.3%
Source: author’s estimations based on WEO data.
27
GDP per capita, constant
LCU
Annual
1960
2012
growth
100
293
2.1%
100
439
2.9%
100
154
0.8%
100
319
2.3%
BLZ
TTO
BRB
JAM
1960
1962
1964
1966
1968
1970
1972
1974
1976
1978
1980
1982
1984
1986
1988
1990
1992
1994
1996
1998
2000
2002
2004
2006
2008
2010
2012
Index of real GDP per capita,
1960=100
500
450
400
350
300
250
200
150
100
50
0
Source: author’s estimations based on WEO data.
Whatever measure of volatility we use, Caribbean economies are very volatile, which is
consistent with other studies, such as Cashin (2004) – who finds Caribbean output volatility
is about 2.8 times greater than that of the United States. 9
Table 5. Volatility in growth
GDP growth (annual %)
Descriptive Statistics
max
min
st. Dev
11.44
-5.08
4.27
15.17
-2.11
3.97
18.01
-6.69
4.79
14.43
-9.20
4.93
C.V
1.65
0.72
3.15
1.51
1st quartile
4th quartile
0.28
5.54
3.12
7.82
-1.31
3.31
0.78
5.78
GDP per capita growth (annual %)
Descriptive Statistics
max
min
st. Dev
BRB
11.048
-5.487
4.291
C.V.
1.970
1st quartile
4th quartile
-0.219
5.053
BRB
BLZ
JAM
TTO
BLZ
JAM
TTO
12.895
16.198
13.886
-4.821
-7.826
-10.644
4.006
4.765
4.915
1.353
8.729
2.074
0.437
-2.132
-0.173
5.705
2.533
4.849
Correlation matrix
BRB
BLZ
BRB
1
BLZ
0.005
1
JAM
0.151 0.227
TTO
0.143 0.185
Correlation matrix
BRB
BLZ
BRB
1
BLZ
0.020
1
JAM
0.137 0.215
TTO
0.140 0.191
JAM
TTO
1
-0.117
1
JAM
TTO
1
-0.100
1
Source: author’s estimations based on WEO data.
We find a high correlation between volatility and the size of the economy and concentration
of the export basket, but even after controlling by these two factors, Caribbean economies
are more volatile than expected.10
Ramey and Ramey (1995), suggest that growth volatility is costly for economic growth.
Figure 3 relates economic growth and output volatility using long time series of real GDP
based on WDI (1961-2012) –it includes a regression line for both variables using the entire
9
The empirical literature uses two main measures of output volatility: (1) the standard-deviation of inflationadjustment growth rates (e.g. Gavin and Hausmann, 1996; Ramey and Ramey, 1995) and (2) the standard
deviation of the gap between actual and trend GDP (e.g. Hnatkovska and Loayza, 2004). In our case, both
measures give the same qualitative results.
10
Regression results available from the authors.
28
sample. It shows SIDS economies have lower growth than expected (conditional on growth
volatility). Among SIDS group of economies, Caribbean countries have the lowest gap on
average. Dominican Republic is the only SIDS economy clearly above the line, with much
higher growth than expected given its volatility.11 Belize is also above the line, being the
less volatile of the four economies we analyzed (not considered a SIDS as it is not an
island). Jamaica is in the other extreme, with the highest volatility and the lowest growth
(both in absolute value and relative to the expected growth given its volatility) of the four
economies we focus on.
Figure 2. Volatility and Growth, 1960-2012
0.1
0.09
Rest of the world
The Caribbean
Pacific
Other Small Island Economies
0.08
0.07
Growth (2)
0.06
BLZ
0.05
0.04
0.03
0.02
TTO
BRB
0.01
JAM
R² = 0.4936
0
0.1
1
10
100
Volatility (1)
-0.01
Notes: (1) volatility is measured as the coefficient of variation of the growth rate for countries with long
enough time series (more than 20 observations) for the period 1961-2012. The axis is re-expressed as the log
of the coefficient of variation. (2) Growth is the annual growth rate of the real GDP for the time window
analyzed for each country. A simple linear regression of (log) growth on volatility has an R2 of 0.62 showing
the high correlation between both variables. See Annex 2 for the list of countries included as SIDS
Source: author’s estimations based on WEO data.
11
A simple regression of growth on volatility and a dummy taking one for SIDS shows this is statistically
significant (at 10% or 5% excluding Dominican Republic). We use the United Nations Department of
Economic and Social Affairs classification to state if a country is DIS or not. According to this definition, in
the Caribbean region there are 23 SIDS: Anguilla, Antigua & Barbuda, Aruba, The Bahamas, Barbados,
Belize, British Virgin Islands, Cuba, Dominica, Dominican Republic, Grenada, Guyana, Haiti, Jamaica,
Montserrat, Netherlands Antilles, Puerto Rico, St Kitts and Nevis, St. Lucia, St. Vincent and the Grenadines,
Suriname, Trinidad & Tobago and United Sates Virgin Islands. http://www.un.org/specialrep/ohrlls/sid/list.htm
29
Next we compute growth crisis for the entire sample based on yearly data as defined in
previous section (negative growth rate for the real GDP per capita at time t and growth
below the pre-shock three-period average). The number of years the economy is facing a
crisis is defined as the frequency of the growth crisis. We relate this measure to volatility
and growth, see Figure 4. First we find a positive correlation between output volatility and
the frequency of growth crisis, which is not entirely surprising as a larger variance (high
volatility) means the distribution has more weight in the tails (more probability of a growth
rate far from the mean). Perhaps more interesting is the negative relationship between the
number of growth crisis and economic growth. This result is related to Hnatkovska and
Loayza (2004) proposition that the negative association between volatility and growth
reflects in fact the harmful impact of sharp negative fluctuations (crisis volatility) rather
than the effect of repeated but small cyclical movements (normal volatility).
Figure 3. Crisis, Volatility and Growth, 1960-2012
Growth Crisis and Growth Volatility
10
12%
9
8
10%
7
8%
Volatility
Average Growth Rate
Growth Crisis and Growth
14%
6%
4%
R² = 0.3144
R² = 0.5468
4
2
0%
-4%
5
3
2%
-2%
6
0
5
10
15
1
20
0
0
# of growth collapses
Growth Crisis and Recession
5
10
15
# of Growth Collapses
Recession and Growth Volatility
30
20
10
16
9
14
8
# of growth collapses
18
12
7
Volatility
R² = 0.6709
10
8
6
5
R² = 0.4684
4
3
4
2
2
1
0
-2
6
0
5
10
15
20
0
25
0
# of years with negative growth
5
10
15
20
# of years with negative growth
25
Source: author’s estimations based on WEO data.
If we look at the histogram of the number of growth crisis (a Poisson distribution), we can
clearly see that SIDS economies have higher expected value, see Figure 4. More than 60%
of the economies in the SIDS group register at least two growth crisis compared to 41% for
the rest of the economies in the 50 years we analyze.
Figure 4. Histogram of Growth Crisis, 1960-2012
(relative frequency in %)
45
40
35
30
25
Rest of the World
20
SIS Economies
15
10
5
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
Source: author’s estimations based on WEO data.
These results are not surprising based on prior evidence. Output volatility is usually
indicated as one of the main characteristics of low-income countries (Hnathovska and
Loayza, 2004; Loayza et al., 2007; Moghadam and Canuto, 2011; among others) and
particularly of island nations (Easterly and Kraay, 2000; Gounder and Saha, 2007).
31
Quarterly Data
In what follows we described the quarterly data we will use for our econometric
estimations. Table 6 shows several descriptive statistics. We considered two standard
measures of output volatility. First, in line with many other empirical studies, output
volatility is calculated as the standard deviation of real GDP growth rates, as reported in
Column (2). Second, we employ the standard deviation of the output gaps (in relation to the
HP-filtered real GDP) as another measure of volatility (see Column (3)). A high standard
deviation may indicate a highly uncertain economy. Belize shows the highest real GDP
annual growth rate (in quarterly basis), but the greatest output variability among the
analyzed countries. However, an economy can be very volatile without having crisis.
Table 6. GDP growth and output volatility: 2000Q1-2011Q4, in percentage
Country
Barbados
Belize
Jamaica
Trinidad & Tobago
Annual
GDP
growth
(1)
1.04
4.56
0.70
1.11
Measures of output volatility
Std. Dev.
GDP growth
(2)
Std. Dev.
Output gap
(3)
VaR
at 5%
(4)
VaR
at 10%
(5)
1.99
4.11
1.22
2.47
2.85
6.44
0.88
1.98
-4.21
-1.89
-2.75
-2.66
-3.69
-1.04
-1.98
-2.40
Emerging and Developing Countries
6.00
0.99
-2.32
3.42
World
3.41
0.95
--0.99
1.39
Source: IMF and Caribbean Centre for Money and Finance
Notes: (1) reports the average annual real GDP growth rate.
(2) reports the average standard deviation of four-quarter real GDP growth.
(3) reports the average standard deviation of four-quarter real output gap.
(4) and (5) report the empirical Value at Risk (VaR) of the real GDP growth at the 5% and 10% level,
respectively.
Table 6 also reports the empirical value-at-risk (VaR) at a 5% and 10% level- Column (4)
and (5). These are the growth rates corresponding to the 5% and 10% percentiles. In
standard VaR terminology, this can be interpreted as the worst growth rate the economy
might suffer with 1-α confidence. This measure of risk can give as a better idea of an
economic crisis event than the standard measures of output volatility.
Table 7 shows growth crisis as defined in Section 2, but using quarterly data.
32
Table 7. Growth crisis events, 2000-2012
Country
Trinidad and
Tobago
Jamaica
Barbados
Belize
2008Q4, 2009Q1,
2010Q1, 2010Q2,
2010Q4, 2011Q1,
2011Q3, 2011Q4,
2012Q1
2001Q4, 2007Q4,
2008Q1, 2008Q2,
2008Q3, 2008Q4,
2009Q1, 2009Q2,
2009Q3, 2009Q4,
2010Q1, 2010Q2,
2011Q3, 2012Q1
2001Q2, 2001Q3,
2001Q4, 2002Q1,
2008Q4, 2009Q1,
2009Q2, 2009Q3,
2009Q4, 2010Q1,
2012Q2
2007Q3, 2009Q2,
2011Q2
9
14
11
3
0.43%
0.40%
0.48%
2.58%
when y=1
-1.68%
-1.53%
-3.29%
-1.77%
when y=0
1.62%
1.48%
2.19%
4.35%
Growth crisis events
(quarters)
Number of crisis
Population growth
2000-2010
Avg GDP
growth
Note: coincidences are marked in bold
From 44 observations Jamaica shows the astonishing number of 14 crisis, being Belize the
less vulnerable in this period. The fact that there are many coincidences in the identified
growth crisis events may indicate that there should be common explanations of economic
vulnerability among Caribbean countries.
These dummy variables that identify the existence of growth crisis events will be the
dependent variables in our econometric models in Section 5.
5. Econometric results
Table 8 reports the estimation results for our model for the four Caribbean countries from
2000 to 2012 in a quarterly basis, and Table 9 the correlation matrix for the best estimation
of the M-probit estimation for each country. The final variables included are those we find
to have the most significant effect (survived our battery of test and specification search).
Results do not change much suing individual probit and M-probit in terms of prediction
power, but there are some small changes in the variables that survive our battery of test,
being the M-probit more parsimonious.
33
Interesting enough, past cumulative growth (last two years annual growth) is very
informative. Once this variable is included most of other variables (such as government
expenditure or debt) are not significant anymore. It works as a state variable, showing
autocorrelation is high. Including cumulative growth makes the models more parsimonious
and has the highest R2. Since our objective is the prediction more than understanding what
leads the process, we include in all the cases cumulative growth as a control variable, so the
interpretation of the rest of the variables should be understood as a marginal effect. Indeed
the variables that (significantly) add probability to this autoregressive process are just a
few. In the case of Trinidad and Tobago most notable the change in the price of WTI and
the change in the risk free rate (the change more than the level). For Jamaica, in addition to
cumulative growth the most important variables are US growth and natural disasters. In the
case of Barbados, once controlling for cumulative growth, the government expenditure and
the international price of sugar have a significant effect. If we add the effect of US
economic growth, the sugar price becomes insignificant. As for Belize, we found out that
external shocks such as commodity prices fluctuations and US economic growth have a
significant effect on the probability of a growth crisis event. We also found a positive effect
of the external debt-to-GDP ratio on Belizean economic vulnerability. This finding is
particularly interesting given the recent problems for Belizean government to pay the debt
(facing a challenge in addressing the debt overhang). For Jamaica, another country which
has recently gone through debt overhang, we do not find a marginal significant effect.
The signs are the expected ones. The variables that increase the probability of entering in a
growth crisis are: increases in the risk free rate, increase in the debt-to-GDP ratio (total debt
or external debt), increases in the government expenditure-to-GDP ratio and natural
disasters. On the contrary, economic vulnerability decreases when there is an increase in
international commodity prices (WTI), an increase in the US growth or high cumulative
growth over the last two years.
In all the cases the models work relatively well, with high predicted power, as we discussed
in more detail later in this paper. A note of caution should be done regarding pseudo R2,
which has not the same interpretation as the standard R2 in OLS regression. In OLS
regressions, R2 shows the percentage of the variation in y explained by the model. The
34
McFadden pseudo R2 in the Probit model shows the ratio of the log likelihood of the model
to the log likelihood of an only constant model. The ratio of the likelihoods suggests the
level of improvement over the intercept model offered by the full model. Of course it helps
to compare alternative models for the same y, as a higher pseudo R2 means the model is
better fit, but the pseudo R2 alone does not say anything about the predicted power of the
model. So pseudo R2 can be used to select the best model for each country, but the
different pseudo R2 for each country cannot be compared. In our setting, a low pseudo R2
for the best model should be interpreted as a case where the optimal predicting model gives
probabilities of occurrence of the bad event that are relatively constant (or that can be
explained by a constant). This is the case for Belize, where pseudo R2 is low, meaning the
probability is not that much affected by the controls we include. This result might be
affected by the fact that Belize has very few crises in the period we analyze -just 3compared to Jamaica that has 14.
It is also interesting to point out that in the two economies with floating exchange rate
(Trinidad and Tobago and Jamaica) all the factors that can induce a growth crisis are
exogenous, whereas in Belize and Barbados, that have a hard peg, fiscal policy or debt
management, both local factors, are significant to predict crisis. This result does not mean
that domestic public policy does not matter for Jamaica and Trinidad and Tobago, but
rather that in the margin are not causing crisis, although might be imbedded in the model
through parameters or previous growth, what in fact means that this are structural factors.
The correlation matrix shows the pattern of correlation between the disturbances of each
equation in an M-probit model. Trinidad and Tobago and Jamaica have positive correlated
unobserved shocks. All the other relationships are negative, such as the correlation between
Belize and Jamaica, which is the strongest. Barbados is the only country that unobserved
shocks do not have a significant correlation with the other countries.
35
Table 8. Probit estimations
Variable
Cumulative Growth (last 2 years)
WTI price quarterly growth
Trinidad
and Tobago
(1)
(2)
-0.27***
[-2.96]
-0.11***
[-3.24]
Jamaica
Barbados
(3)
(4)
(5)
(6)
-0.22**
[-2.54]
-0.27***
[-3.10]
-0.06***
[-2.55]
-0.04*
[-1.87]
-0.01*
[-1.67]
1.86*
[1.61]
0.03**
[2.41]
Debt / GDP
0.09**
[2.37]
External Debt / GDP
0.19**
[1.92]
-0.25**
[-2.08]
Government Expenditure / GDP
U.S. GDP annual growth
0.98**
[2.45]
-1.14***
[-2.61]
Caribbean Natural Disasters (=1)
-1.49***
[-2.57]
0.12**
[2.07]
Sugar price
Constant
(7)
-0.05**
[-2.38]
WTI price annual growth
U.S. Treasury Bill 10-years quarterly growth
Belize
-0.90*
[-1.91]
-5.33***
[-4.47]
1.78**
[2.04]
0.23
[0.25]
-0.09**
[-2.15]
-3.17*
[-1.70]
0.53*
[1.84]
-0.67***
[-3.16]
-0.19**
[-2.13]
-0.03
[-1.33]
-1.95
[-0.89]
-7.86***
[-2.80]
Log likelihood
-8.56
-8.56
-12.70
-10.78
-11.51
-7.79
-8.13
Pseudo R2
0.62
0.39
0.52
0.59
0.45
0.63
0.28
Correctly classified
86.36%
80.39%
93.02%
86.05%
86.36%
93.18%
95.83%
Predicted probability
0.205
0.174
0.307
0.304
0.179
0.183
0.064
Observed probability
0.204
0.173
0.302
0.302
0.182
0.182
0.063
y=1
9
9
14
14
11
11
3
y=0
35
42
29
29
33
33
45
Note: *, ** and *** indicate significance at 10%, 5% and 1% level, respectively. Z-statistics are reported in brackets. Robust standard errors are computed.
36
Table 9. M-Probit estimations and correlation matrix
Constant
Cumulative Growth
WTI price growth
Terms of Trade
Trinidad and
Tobago
1.86
[1.48]
-0.28***
[-3.51]
-0.09**
[-2.06]
-0.02**
[-1.97]
Jamaica
Barbados
Belize
-0.33
[-1.49]
-0.27***
[-3.65]
-2.90*
[-1.89]
-1.08***
[-3.67]
-0.67***
[-3.90]
0.11*
[1.90]
-0.23***
[-2.97]
U.S. GDP growth
External Debt / GDP
Correlation matrix (V) – off-diagonal elements
Jamaica
0.61**
Barbados
-0.38
Belize
-0.52***
-0.11
-0.72***
-0.19
0.31
0.30
0.19
0.18
LR test: χ2(6) = 8.070 [p-value=0.233]
Predicted Marginal Prob.
Observed Marginal Prob.
0.22
0.21
0.09
0.07
Predicting shocks
The next set of figures compares the predicted conditional probabilities to actual events for
each country. Growth rate shows the annual growth rate for each quarter, vulnerability is
the prediction of our probit model, and the shadow areas are the years we identified with
growth crisis.
Figure 5. Model predictions. Barbados
1.2
Growth Crisis
Vulnerability
1
8
6
4
0.8
2
0.6
0
-2
0.4
-4
0.2
-6
0
-8
2002 2002 2003 2003 2004 2004 2005 2005 2006 2006 2007 2007 2008 2008 2009 2009 2010 2010 2011 2011 2012 2012
Q1 Q3 Q1 Q3 Q1 Q3 Q1 Q3 Q1 Q3 Q1 Q3 Q1 Q3 Q1 Q3 Q1 Q3 Q1 Q3 Q1 Q3
37
Figure 6. Model predictions. Belize
1.2
Growth Crisis
Vulnerability
1
20
15
0.8
10
0.6
5
0.4
0
0.2
0
-5
2002 2002 2003 2003 2004 2004 2005 2005 2006 2006 2007 2007 2008 2008 2009 2009 2010 2010 2011 2011 2012 2012
Q1 Q3 Q1 Q3 Q1 Q3 Q1 Q3 Q1 Q3 Q1 Q3 Q1 Q3 Q1 Q3 Q1 Q3 Q1 Q3 Q1 Q3
Figure 7. Model predictions. Jamaica
1.2
Growth Crisis
Vulnerability
1
5
4
3
2
0.8
1
0.6
0
-1
0.4
-2
-3
0.2
-4
0
-5
2002 2002 2003 2003 2004 2004 2005 2005 2006 2006 2007 2007 2008 2008 2009 2009 2010 2010 2011 2011 2012 2012
Q1 Q3 Q1 Q3 Q1 Q3 Q1 Q3 Q1 Q3 Q1 Q3 Q1 Q3 Q1 Q3 Q1 Q3 Q1 Q3 Q1 Q3
Figure 8. Model predictions. Trinidad and Tobago
1.2
Growth Crisis
Vulnerability
1
8
6
4
0.8
2
0.6
0
0.4
-2
0.2
-4
0
-6
2002 2002 2003 2003 2004 2004 2005 2005 2006 2006 2007 2007 2008 2008 2009 2009 2010 2010 2011 2011 2012 2012
Q1 Q3 Q1 Q3 Q1 Q3 Q1 Q3 Q1 Q3 Q1 Q3 Q1 Q3 Q1 Q3 Q1 Q3 Q1 Q3 Q1 Q3
38
Probit or M-Probit
Although we do find correlation in unobserved shocks, the likelihood-ratio test indicates
that our multivariate model does not significantly fit better than individual probit models.
Next figure summarizes the predictions for each country according to the two estimation
techniques used.
Figure 9. Vulnerability according to the different estimation methods
Barbados
Belize
1
1
0.9
0.9
0.8
0.8
0.7
0.7
Probit
M-Probit
2011 Q1
2011 Q4
2012 Q3
2011 Q4
2012 Q3
2010 Q2
2009 Q3
2008 Q4
2008 Q1
2007 Q2
2006 Q3
2011 Q1
Jamaica
2005 Q4
2005 Q1
2002 Q1
2012 Q3
2011 Q4
2011 Q1
2010 Q2
2009 Q3
2008 Q4
2008 Q1
2007 Q2
0
2006 Q3
0.1
0
2005 Q4
0.1
2005 Q1
0.2
2004 Q2
0.3
0.2
2003 Q3
0.3
2002 Q4
0.4
2002 Q1
0.4
2004 Q2
0.5
2003 Q3
0.5
M-Probit
0.6
2002 Q4
0.6
Probit
Trinidad and Tobago
1
1
0.9
2010 Q2
2009 Q3
2008 Q4
2008 Q1
M-Probit
2002 Q1
2012 Q3
2011 Q4
2011 Q1
2010 Q2
2009 Q3
0
2008 Q4
0.1
0
2008 Q1
0.1
2007 Q2
0.2
2006 Q3
0.3
0.2
2005 Q4
0.3
2005 Q1
0.4
2004 Q2
0.5
0.4
2003 Q3
0.5
2002 Q4
0.6
2002 Q1
0.6
Probit
2007 Q2
0.7
2006 Q3
M-Probit
2005 Q4
0.7
2005 Q1
0.8
2004 Q2
Probit
2003 Q3
0.8
2002 Q4
0.9
Evaluation criteria
Our probit model produces estimated probabilities of growth crises. High probabilities
signal crises, low probabilities normal periods. A model may indicate a growth crisis event
(high estimated probability) when a crisis indeed occurs or the model does not predict a
39
crisis and no crisis occurs. However, the models might give false signals. That is, the model
does not signal a crisis (low estimated probability) when in fact a crisis occurs (Type I
error) or it may indicate a crisis when no crisis actually takes place (Type II error).
Therefore, we evaluate the overall performance of the estimated models. Instead of using
an ad-hoc assumption on the translation of estimated crisis probabilities into crisis dummies
as in Dabla-Norris and Günduz (2014), we use two measures to evaluate the accuracy of the
estimations: the quadratic probability score (QPS) and the log probability score (LPS) (see
Diebold and Rudebusch, 1989). Both scores give an indication of the average closeness of
the predicted probabilities on the observed realizations, the latter a dummy variable that
takes on a value of one when there is a crisis and zero otherwise. The QPS and the LPS are
given by,
𝑇
1
𝑄𝑃𝑆 = ∑ 2(𝑃𝑡 − 𝑍𝑡 )2
𝑇
𝑡=1
𝑇
1
𝐿𝑃𝑆 = − ∑((1 − 𝑍𝑡 ) ln(1 − 𝑃𝑡 ) + 𝑍𝑡 ln(𝑃𝑡 ))
𝑇
𝑡=1
where Pt is the estimated probability of the occurrence of crisis event by the model in
period t and Zt equals one if the event occurs in period t and equals zero otherwise.
The QPS ranges from 0 to 2, with a score of 0 corresponding to perfect accuracy and a
score of 2 shows that the model indicates a perfect false signal. LPS ranges from 0 to ∞,
with LPS=0 being perfect accuracy and LPS=∞ being perfect false signal.
The QPS has the desirable property of being strictly proper, meaning that it achieves a strict
minimum under the truthful revelation probabilities by the forecaster. It is a function only
of the discrepancy between realizations and assessed probabilities. The LPS depends
exclusively on the probability forecast of the event that actually occurred, assigning as a
score the log of the assessed probabilities. Therefore, LPS and QPS imply different loss
functions, the LPS penalizes large mistakes more heavily than the QPS.
40
We evaluate crisis probabilities over the whole sample: 2002Q1-2012Q4. Table 8 reports
the overall performance in terms of the QPS and LPS for each estimated probit model (both
individual and multivariate).
Table 8. Overall performance: 2002Q1-2012Q4
Country
Probit model
QPS
LPS
Trinidad and Tobago
Individual (1)
0.137
0.195
Individual (2)
0.201
0.285
Multivariate
0.132
0.189
Individual (3)
0.182
0.326
Individual (4)
0.160
0.276
Multivariate
0.276
0.428
Individual (5)
0.175
0.262
Individual (6)
0.108
0.177
Multivariate
0.135
0.226
Individual (7)
0.089
0.169
Multivariate
0.122
0.236
Jamaica
Barbados
Belize
Note: individual models correspond to those reported in Table 4, while multivariate
models were reported in Table 5.
Recall that the closer the score statistics are to zero, the more accurate the model
predictions. Therefore, it is not clear that the multivariate model outperforms the individual
probit model. Only for Trinidad and Tobago, the multivariate model had a lower
discrepancy between realizations and assessed probabilities than the estimated individual
models.
Finally, in terms of the predicted power of each case analyzed, Belize shows the best
performance, follows by Barbados, Trinidad and Tobago and Jamaica. It should
41
5.1. Comparison with other Vulnerability Indices
Comparing our results with other indexes is not simple, as our index is a conditional one, so
it depends on which vectors of controls we evaluate the index (probability is not constant
but rather changes across time). Next table compares our results for the mean probability
predicted for the two models we estimate (M-probit and probit) with two of Briguglio’s
works. We also include as reference, second column, the number of growth crisis we
registered in the time window we study.
Table 9. Comparing Results
Barbados
Number
of
growth
crisis
11
Briguglio and
Galea (2003)
EVIAR
EVI
Briguglio et al (2008)
0.672
0,549
Resilience
Index
0.741
M Probit
Probit
Vulnerabil
ity Index
0.717
Predicted
Probability
0.19
Predicted
Probability
0,183
Belize
3
0.762
0,588
0.478
0.768
0.09
0,064
Jamaica
14
0.820
0,706
0.420
0.922
0.31
0,304
T&T
9
0.651
0,408
0.603
0.533
0.22
0,205
The different indices locate Jamaica as the most vulnerable, but for the rest of the countries
the results differ. In particular, Briguglio’s works rank Jamaica, Belize, Barbados and
Trinidad and Tobago, in decreasing order, but our work ranks Jamaica, Trinidad and
Tobago, Barbados and Belize. If we look at the number of quarters with growth crisis, the
ranking is Jamaica, Barbados, Trinidad and Tobago and Belize, which is more consistent
with our results. In particular the main difference is Belize, the second most vulnerable in
Briguglio’s work, but with just 3 growth crisis in the 52 quarters we study. Trinidad and
Tobago, on the other hand, is supposed to be the second less vulnerable of the four
economies according to Briguglio’s but in our indices it shows up as the second most
vulnerable (even though we observed less growth crisis than Barbados).. In our estimations
the mean probability is showing that both Barbados and Trinidad and Tobago become more
vulnerable after the 2008 financial crisis, but Trinidad and Tobago has become more
vulnerable.
42
Another interesting difference between the ad-hoc indexes and the regression-based indexes
as the one we develop is that in our model the probability is not constant. As the following
figure shows, the relative ranking according to vulnerability (probability of entering in a
growth crisis) is changing. For instance, in the last two years of the time window we
analyze, the most vulnerable country is Trinidad and Tobago, which before the 2008 crisis
was among the less vulnerable.
Figure 10. Vulnerability across time
100%
90%
80%
70%
60%
50%
T&T
40%
30%
Barbados
20%
10%
Jamaica
2002 Q1
2002 Q3
2003 Q1
2003 Q3
2004 Q1
2004 Q3
2005 Q1
2005 Q3
2006 Q1
2006 Q3
2007 Q1
2007 Q3
2008 Q1
2008 Q3
2009 Q1
2009 Q3
2010 Q1
2010 Q3
2011 Q1
2011 Q3
2012 Q1
2012 Q3
0%
Belize
Note: predicted probabilities for the simple probit model based on the best fit model estimated
according to Table 6.
43
6. Conclusions
In this work we discuss how to measure growth vulnerability for Caribbean economies. We
find the M-probit approach the most suitable given data restrictions, although estimation
results do not show significant difference with estimating individual probits for each
country.
Our estimations show that a few variables can very successfully predict growth
vulnerability in these economies, after controlling by the past economic growth. The
control variables are not the same across countries, what stresses the limitation of cross
country studies, which impose a rigid structure. The importance of past growth shows the
autocorrelative importance of shocks.
After controlling by previous economic growth, the variables that help to explain a growth
crisis are mostly related to external shocks. The source of the shock though is different in
each economy. For instance, in Trinidad and Tobago, where more than 50% of the GDP is
originated by the petroleum industries, the relevant variable is the petroleum price. In the
case of Jamaica, an importer of petroleum and exporter of bauxite, the most relevant
variable is the GDP growth of the US, a variable related with the demand for tourism (its
most significant economic growth driver). Natural disasters, shocks that are difficult to
predict if the vulnerability index wants to be projected, are significant only for Jamaica.
The domestic variables were not significant to predict a crisis in Jamaica and Trinidad and
Tobago, both having floating exchange rate system, what might mean automatic stabilizers
are working through monetary policy. On the other hand, fiscal policy and debt
management is significant to explain in the margin a growth crisis in Barbados and Belize,
conditional on previous economic growth. In these countries with a hard peg currency, it
seems all the stabilization effort is through fiscal policy and perhaps this is the reason for
our results. These results by no means are saying that domestic policies do not matter to
prevent shocks, but rather that these domestic factors are structural, incorporated in the
variable that adjust for previous economic growth and in the parameters of the model. In
other words, once one of the external shocks hit the economy, there is little that
44
contemporary economic policy can do to avoid the economic recession, if these policies
were not taken in advance.
One burden of the model we propose is the intensity in the search of the optimal predicting
model and the need to understand very well the economy, in order to propose controls that
are relevant. The advantage is that the model itself says which variable is important and
which not.
A limitation of this work has been data availability, which is a problem in the Caribbean in
general. When longer timer series are available, the model can be estimated with more
accuracy and perhaps enrich the control set. Nevertheless, the predicted power of the
current model is relatively good. An interesting extension would be to combine the probit
model with high frequency data to be able to estimate vulnerability at a high frequency.
Other interesting extensions would be to test the model in other economies, and see what
difference between SIDS arises.
45
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49
Annex 1. Vulnerability indices for the Caribbean
Table shows the relative position of Caribbean countries using different vulnerability
indices. Caribbean economies, and in particular Small Island Developing States (SIDS) are
highly vulnerable. For instance, among the SIDS, Barbados, Jamaica, Saint Lucia and
Trinidad & Tobago are categorized as extremely vulnerable to natural environment.
The relative position of the countries depends on the index used. According to Briguglio
and Galea (2003) Jamaica is the most vulnerable economically of the group, followed by
Belize, Barbados and T&T; but according to environmental vulnerability (UNEP),
Barbados is the most vulnerable, and Belize the least.
Table 5. Vulnerability Indices for the Caribbean region
EVIAR.
Index
Country / Source
Antigua and Barbuda
Barbados
Belize
Costa Rica
Cuba
Dominican Republic
El Salvador
Grenada
Honduras
Jamaica
Mauritius
Nicaragua
Panama
Saint Kitts & Nevis
Saint Lucia
Saint Vincent & the Grenadines
Trinidad & Tobago
Venezuela
Briguglio
and Galea
(2003)
-0.672
0.762
0.620
--0.617
0.777
0.696
0.820
0.699
0.715
0.782
0.756
0.833
0.790
0.651
0.627
Economic
Vulnerability
Index
Briguglio
and Galea
(2003)
0,549
0,588
0,334
0,277
0,645
0,409
0,706
0,484
0,442
0,64
0,685
0,765
0,647
0,408
0,356
Social
Vulnerability
Index
St. Bernard
(2007)
Environmental
Vulnerability
Index
SOPAC and
UNEP[1]
--0.473
----0.496
-----0.421
-0.456
---
307 (V)
403 (EV)
258 (A)
354 (HV)
329 (HV)
324 (HV)
348 (HV)
316 (HV)
273 (V)
381 (EV)
358 (HV)
272 (V)
247 (AR)
359 (HV)
393 (EV)
337 (HV)
381 (EV)
291 (V)
Note: (R) resilient, (AR) at risk, (V) vulnerable, (HV) highly vulnerable, (EV) extremely vulnerable. EVIAR:
Economic Vulnerability Index Augmented by Resilience.
50
Annex 2.
Table A1. List of Small Island Economies
CARIBBEAN
Anguilla
Antigua and Barbuda
Aruba
Bahamas, The
Barbados
Belize
British Virgin Islands
Cuba
Dominica
Dominican Republic
Grenada
Guyana
Haiti
Jamaica
Montserrat
Netherlands Antilles
Puerto Rico
Saint Kitts and Nevis
Saint Lucia
Saint Vincent and the Grenadines
Suriname
Trinidad and Tobago
United States Virgin Islands
PACIFIC
American Samoa
Cook Islands
Federated States of Micronesia
Fiji
French Polynesia
Guam
Kiribati
Marshall Islands
Nauru
New Caledonia
Niue
Northern Mariana Islands
Palau
Papua New Guinea
Samoa
Solomon Islands
Timor-Leste
Tonga
Tuvalu
Vanuatu
51
REST OF SIE
Bahrain
Cape Verde
Comoros
Guinea-Bissau
Maldives
Mauritius
São Tomé and Príncipe
Seychelles
Singapore