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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] 1 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 2 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). 3 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. 4 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). 3 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. 5 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. 6 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. 7 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). 8 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. 9 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) 10 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 11 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 12 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 tDk 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 13 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) t21 ( 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. 14 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. 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(2007) ‘Vulnerability assessment of developing countries: the case of small‐ island developing states’, Development Policy Review, 25: 243-264. United Nations (1994) ‘Developing a Vulnerability Index for SIDS’ UNDP (2013) ‘Human Development Report 2013’, United Nations Development Programme. 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