Download Vulnerability in Small Island Economies. The case of the

Vulnerability in Small Island Economies.
The case of the Caribbean
Sebastian Auguste (UTDT) and Magdalena Cornejo
(UTDT)
Objective
 To develop a vulnerability index for the Caribbean that:
 Captures the specificities of each country.
 Includes exposure and resilience.
 Changes over time
 Suitable to the statistics available for the region
 Can be used for policy making (indicating when the economy is
more vulnerable to shocks so the Government can react).
Some definitions
 Vulnerability: Guillaumont (2010), economic vulnerability is
the risk of a (usually poor) country seeing its development
hampered by a natural disaster or external shocks.
 Vulnerability vs Volatility:
 Vulnerability: the probability of having a very bad event,
related to the concept of Value at Risk (VaR) in finance
 Volatility: risk of being away from the expected value, concept
of standard deviation or coefficient of variation in finance.
 According to Briguglio Vulnerability can be seen as the result
of three components:
 the size and frequency of the exogenous shocks, either observed
(ex post vulnerability) or anticipated (ex ante vulnerability);
(e.g. whether there are tropical storms or not)
 exposure to shocks; (e.g. whether the tropical storm can hit the
country)
 the capacity to react to shocks, or resilience (e.g. whether the
tropical storms that hit the country generates losses or not)
3 ways to build indexes
1. Ad Hoc Indices
 Briguglio (1992, 1995, 1997, 2002), United Nations (1994), Briguglio
and Galea (2003); Briguglio et al.(2009), Atkins, Mazzi and Easter
(2000), Liou and Ding (2004), Easter (1998) , Turvey (2007), Crowards
(2000) , St. Bernard (2007).
 Advantages: easy to construct, easy to compare countries as the
same index is build for all the countries
 Limitations: it captures the size and frequency of the schock,
but not exposure or resilience.
2. Two tier approach
 Variables to be included are selected based on econometric
analysis, that links an output variable (e.g. GDP, poverty,
etc.) with potential factors.
 Only those factors that are significant are latter included in
the index.
 Examples. Atkins et al., 2000; Guillaumont and Chauvet,
2001; Peretz et al., 2001)
3. Probit (Regression based index)
 A simple way to do a regression based index is using a Probit model
(defining a variable that takes 1 when there is crisis, 0 no crisis).
 Once the probit model is estimated, the same model can be used to
predict future crisis.
 More related to the literature on Early Warning Systems (EWS)
 Advantage:
 Only shocks that are capable of affecting the economy are significant
 It captures the three concepts of vulnerability endogenously (the variable is
the shock, its coefficient is the resilience)
 It is non linear (so the different shocks have interactions)
 It focus only on the bad events (linear regression is looking at the volatility of
a variable)
 Examples. Easterly and Kraay (2000), IMF (2011) and Dabla-Norris and
Gündüz (2014), Méndez Quesada and Solera Ramírez (2004)
Our Model
 M-equation multivariate probit model (M=4 countries),
*
yim
  m ' X im   im ,
m  1,..., M
*
yim  1 if yim
 0 and 0 otherwise
 yim=1 if the bad event is observed (growth collapse) in country
m in quarter i.
 A prediction is given by
Data
 Quarterly GDP data, available only for Barbados, Belize, Jamaica
and Trinidad and Tobago.
 Growth crisis (similar to Dabla-Norris and Gündüz, 2014)
happens if:
1.
2.

the post-shock two-period average level of real GDP falls below the
pre-shock three-period trend, and
the real GDP annual growth rate is below the long-run population
growth at time t (i.e. per capita GDP growth is negative).
Note: that by taking annual growth with our quarterly data, we
eliminate the problems of adjusting to seasonability.
Frequency of GDP Crisis
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
X variables
Chosen to capture foreign and domestic sources affecting GDP
Variable
Description
Source
Commodity prices
Real prices of oil (WTI), banana, sugar in U.S. dollars
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
The results
Variable
Cumulative Growth (last 2 years)
WTI price quarterly growth
T&T
Jamaica
Barbados
-0.27***
[-2.96]
-0.11***
[-3.24]
-0.27***
[-3.10]
-0.04*
[-1.87]
-0.01*
[-1.67]
WTI price annual growth
U.S. Treasury Bill 10-years quarterly growth
Belize
1.86*
[1.61]
Debt / GDP
0.09**
[2.37]
External Debt / GDP
0.53*
[1.84]
-0.67***
[-3.16]
-0.19**
[-2.13]
0.23
[0.25]
-0.03
[-1.33]
-1.95
[-0.89]
-7.86***
[-2.80]
-10.78
0.59
86.05%
0.304
0.302
14
29
-7.79
0.63
93.18%
0.183
0.182
11
33
-8.13
0.28
95.83%
0.064
0.063
3
45
Government Expenditure / GDP
-1.49***
[-2.57]
0.12**
[2.07]
U.S. GDP annual growth
Caribbean Natural Disasters (=1)
Sugar price
Constant
-0.90*
[-1.91]
Log likelihood
Pseudo R2
Correctly classified
Predicted probability
Observed probability
y=1
y=0
-8.56
0.62
86.36%
0.205
0.204
9
42
 T&T. The two sources of growth crisis are foreign shocks: WTI price
and US interest rates.
 Jamaica. Two main sources: Natural disasters or a fall in US GDP.
 Barbados: change in government expenditure and US GDP
 Belize: WTI , US GDP and External Debt
 Remark: Only in Barbados and Belize a domestic variable can affect growth, both
related to fiscal policy. Remember both countries have a hard peg with US dollar, so
coping with shocks is basically done through fiscal policy. In both cases more
expenditure or more debt is related with more vulnerability.
 For Jamaica and T&T, that have floating exchange rate, fiscal policy variables are not
significant, and the floating exchange rate seems to be the stabilizer.
 Finally, in terms of the predicted power of each case analyzed, Belize shows the best
performance, follows by Barbados, Trinidad and Tobago and Jamaica
1.2
2002 Q1
2002 Q2
2002 Q3
2002 Q4
2003 Q1
2003 Q2
2003 Q3
2003 Q4
2004 Q1
2004 Q2
2004 Q3
2004 Q4
2005 Q1
2005 Q2
2005 Q3
2005 Q4
2006 Q1
2006 Q2
2006 Q3
2006 Q4
2007 Q1
2007 Q2
2007 Q3
2007 Q4
2008 Q1
2008 Q2
2008 Q3
2008 Q4
2009 Q1
2009 Q2
2009 Q3
2009 Q4
2010 Q1
2010 Q2
2010 Q3
2010 Q4
2011 Q1
2011 Q2
2011 Q3
2011 Q4
2012 Q1
2012 Q2
2012 Q3
Model predictions. Trinidad and Tobago
Growth Crisis
Vulnerability
Growth Rate (secondary axis)
1
0.2
0
8
6
4
0.8
2
0.6
0
0.4
-2
-4
-6
1.2
2002 Q1
2002 Q2
2002 Q3
2002 Q4
2003 Q1
2003 Q2
2003 Q3
2003 Q4
2004 Q1
2004 Q2
2004 Q3
2004 Q4
2005 Q1
2005 Q2
2005 Q3
2005 Q4
2006 Q1
2006 Q2
2006 Q3
2006 Q4
2007 Q1
2007 Q2
2007 Q3
2007 Q4
2008 Q1
2008 Q2
2008 Q3
2008 Q4
2009 Q1
2009 Q2
2009 Q3
2009 Q4
2010 Q1
2010 Q2
2010 Q3
2010 Q4
2011 Q1
2011 Q2
2011 Q3
2011 Q4
2012 Q1
2012 Q2
2012 Q3
Model predictions. Jamaica
Growth Crisis
Vulnerability
Growth Rate (secondary axis)
1
0.8
0.6
0.4
0.2
0
5
4
3
2
1
0
-1
-2
-3
-4
-5
1.2
2002 Q1
2002 Q2
2002 Q3
2002 Q4
2003 Q1
2003 Q2
2003 Q3
2003 Q4
2004 Q1
2004 Q2
2004 Q3
2004 Q4
2005 Q1
2005 Q2
2005 Q3
2005 Q4
2006 Q1
2006 Q2
2006 Q3
2006 Q4
2007 Q1
2007 Q2
2007 Q3
2007 Q4
2008 Q1
2008 Q2
2008 Q3
2008 Q4
2009 Q1
2009 Q2
2009 Q3
2009 Q4
2010 Q1
2010 Q2
2010 Q3
2010 Q4
2011 Q1
2011 Q2
2011 Q3
2011 Q4
2012 Q1
2012 Q2
2012 Q3
Model predictions. Belize
Growth Crisis
Vulnerability
Growth Rate (secondary axis)
1
0.2
0
20
15
0.8
10
0.6
5
0.4
0
-5
1.2
2002 Q1
2002 Q2
2002 Q3
2002 Q4
2003 Q1
2003 Q2
2003 Q3
2003 Q4
2004 Q1
2004 Q2
2004 Q3
2004 Q4
2005 Q1
2005 Q2
2005 Q3
2005 Q4
2006 Q1
2006 Q2
2006 Q3
2006 Q4
2007 Q1
2007 Q2
2007 Q3
2007 Q4
2008 Q1
2008 Q2
2008 Q3
2008 Q4
2009 Q1
2009 Q2
2009 Q3
2009 Q4
2010 Q1
2010 Q2
2010 Q3
2010 Q4
2011 Q1
2011 Q2
2011 Q3
2011 Q4
2012 Q1
2012 Q2
2012 Q3
Model predictions. Barbados
Growth Crisis
Vulnerability
Growth Rate (secondary axis)
0.6
0
8
6
1
4
0.8
2
0
-2
0.4
-4
0.2
-6
-8
Vulnerability
100%
90%
80%
70%
60%
50%
40%
30%
T&T
Barbados
20%
10%
0%
Jamaica
Belize
Comparing Results
Number
of
Briguglio and
Galea (2003)
growth EVIAR
collapses
EVI
Briguglio et al (2008)
M Probit
Resilience Vulnerabil Predicted
Index
ity Index Probability
Probit
Predicted
Probability
Barbados
11
0.672
0,549
0.741
0.717
0.19
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
Belize is considered according to Briguglio’s works the second most vulnerable, but it
shows the lowest frequency of collapses. In our model it is the less vulnerable. Only for
Jamaica the relative ranking coincides
Conclusions
 We elaborate a regression based vulnerability index, based on recent
works that use probit model, more in the spirit of how vulnerability is
measured in microeconomics.
 It has the advantage of
 been suitable for the Caribbean with serious data limitations,
 providing a time variant index, that can be used to know when the economy
in entering in red area
 Including resilience endogenously
 Our difference with previous works in this area are:
 Use of panel data with quarterly information
 Use of an M-Probit model
 Include time series aspects (state variables)