The economic vulnerability of SIDS, which is well documented,
stems from a number of inherent characteristics of such states,
- The small size of SIDS, which limits their ability to reap the
benefits of economies of scale and constrains production possibilities.
- A high degree of economic openness rendering these states particularly
susceptible to economic conditions in the rest of the world.
- Dependence on a narrow range of exports, giving rise to the usual
risks associated with lack of diversification.
- Dependence on imports, in particular energy and industrial supplies,
exacerbated by limited import substitution possibilities.
- Insularity, peripherality and remoteness, leading to high transport
costs and marginalization.
Small island states tend also to be environmentally vulnerable,
mainly due to:
- Limited assimilative and carrying capacity, leading to problems
associated with waste management, water storage and other factors
affected by small territorial size.
- A relatively large coastal zone, in relation to the land mass,
making these states especially prone to erosion.
- Fragile ecosystems, because of low resistance to outside influences,
endangering endemic species of flora and fauna.
- A proneness to natural disasters, including earthquakes, volcanic
eruptions, cyclones, hurricanes, floods, tidal waves and others
these disasters also affect larger territories, but the impact
is more devastating on a small island state.
- A relatively high proportion of land which could be affected by
climate change, and in particular a rise in sea level, possibly
resulting in proportionately large land losses, particularly in
- The significant impact on the environment of economic development,
often leading to a fast depletion of agricultural land and natural
Vulnerability and economic success
In spite of their economic and environmental vulnerability, many
small states register relatively high GNP per capita compared
with other developing countries. (There are, of course, a number
of SIDS which have low GNP per capita and are classified as least
developed states.) High GNP per head gives the impression of economic
strength, and masks the fact that economic success may be quite
fragile and dependent to a high degree on conditions outside the
This has led to the development of an economic vulnerability index,
the main objective being to highlight the underlying economic
and environmental fragility of many small states.
Constructing the vulnerability index
There are three basic methods for computing a vulnerability index:
- Normalization procedure.
- Mapping on a categorical scale.
- Regression method.
The method most commonly used is to obtain data for the components
of the index, with each component representing a facet of vulnerability.
Since the components of the index are often measured in different
units, the observations have to be standardized or normalized
to permit averaging, with the average being called a composite
The normalization procedure most commonly used is that which adjusts
the observation to take a value of between 0 and 1 using the formula:
Vij = (Xij - MinXi) / (MaxXi - MinXi), where: Vij stands for the
standardized observation associated with the ith component for
country j; Xij stands for the value of the ith component in the
vulnerability index, for country j; MaxXi and MinXi stand for
the maximum and minimum value of the ith component for all countries
in the index. Crowards refined the method to reduce the undue
impact of outliers on the distribution of the observations, by
assigning the value of 1 to the top decile of values in the observations
of a particular variable and a value of 0 to the bottom decile.
The averaging procedure can be based on equal or varying weights
assigned to each component. Briguglio and Crowards experimented
with varying weights for each component, but their preferred method
was that involving equal weights.
Composite indices using this methodology were those by Briguglio
(1992; 1993; 1995; 1997), Chander (1996) and Wells (1996) (see
selected reading). All these studies concluded that small island states tend to
be more vulnerable than larger territories.
The most important shortcoming of this method is that the weights
for averaging the components of vulnerability are arbitrarily
chosen, and that the distribution of the normalized variables
are heavily influenced by outlier observations.
Mapping on a categorical scale
This method, suitable for qualitative data, involves mapping the
scores on a categorical scale ranging from the lowest possible
incidence to the highest. This approach was used in the study
by Kaly et al., where the scale set was 1 to 7. The scores for each component
of the index were averaged to derive a composite index for each
country. Kaly et al. applied the index to three countries only, namely Australia,
Fiji and Tuvalu, since the exercise was a preliminary one and
constrained by the funding provided. The results show that Tuvalu
is the most environmentally vulnerable while Australia is the
least environmentally vulnerable. Again, with this method, there
is a degree of arbitrariness and subjectivity in assigning scores
and in weighting the components of the index.
The regression method
The third method used for constructing the vulnerability index,
proposed by Atkins et al. (1998) and Wells (1997), is based on a regression procedure.
Wells and Atkins et al. assumed that GDP volatility is a manifestation of vulnerability
and can therefore be taken as a proxy of vulnerability. They then
regressed GDP volatility on a number of explanatory variables
which represented causes of vulnerability. The coefficients on
the explanatory variables of estimated equation were then taken
as weights for averaging the three vulnerability components.
This method lets the data produce the weights and does not require
the normalization of the observations. However it has a number
of methodological defects, which limit the operationality and
the reliability of the index. The most important defect is that
the authors had to assume that the dependent variable (namely
GDP volatility) is a proxy for vulnerability, and therefore they
had no need to go through a cumbersome regression procedure to
compute the vulnerability index. An additional problem with the
Atkins et al. method is that the predictive ability of the model is poor.
Components of the index
The vulnerability indices developed so far differ also in terms
of complexity. The economic vulnerability indices generally include
a relatively small number of variables, often limited to three
or four. One reason for this is that many economic variables are
correlated with each other and one variable could be used to represent
others. Other reasons are that many variables complicate the procedure
and data for certain variables are not available across countries.
The most frequent variables used in the economic vulnerability
indices relate to economic openness, export concentration, dependence
on imports of energy and peripherality. Another variable used
is proneness to natural disasters, which is grouped here with
The environmental vulnerability index developed by Kaly et al. utilized a large number of variables (57 in all) since, as argued
by the authors, a large number of indicators are required for
complex ecological systems. Two other attempts at constructing
an environmental vulnerability index were those by Pantin (1997)
and by Atkins et al. (1998). Pantins work is essentially an attempt to measure the
effects of natural disaster on the GDP of the different countries.
The Atkins et al. study was tentative and only introduced as an footnote to their
study on economic vulnerability, but it indicated that small island
developing states tend to be more economically vulnerable than
Kaly et al. attempted to capture three aspects of environmental vulnerability,
- The level of risks (or pressures) which act on the environment
forming the risk exposure sub-index (REI).
- Intrinsic resilience of the environment to risks, forming the
intrinsic resilience sub-index (IRI) which refers to characteristics
of a country which would tend to make it less/more able to cope
with natural and anthropogenic hazards.
- Extrinsic vulnerability or resilience as a result of external
forces acting on the environment, forming the environmental degradation
sub-index (EDI) which describes the ecological integrity or level
of degradation of ecosystems. (This included 39 indicators of
risk (REI), five indicators of resilience (IRI) and 13 indicators
of environmental integrity or degradation (EDI).)
To date there has not been a serious attempt to create a super-composite
index which combines environmental and economic vulnerability.
Benefits of the index
There are at least two benefits that can be derived from the construction
of a composite vulnerability index:
- The index can draw attention to the issue of economic and environmental
vulnerability of SIDS (depending on the aspect which the index
is supposed to measure).
- The index presents a single-value measure of vulnerability based
on meaningful criteria which can be considered by donor countries
and organizations when taking decisions regarding the allocation
of financial and technical assistance or for assigning special
status to SIDS.
The indicators share a number of weaknesses, principally associated
with the subjectivity in their computation, in particular with
regard to the choice of variables, the method of measurement and
the averaging procedure.
Subjective choice of variables
The question of subjective choice of variables is difficult to
resolve. This is, however, not a problem peculiar to the vulnerability
indices but to most empirical work, especially that which purports
to quantify data which is essentially qualitative.
Problems of measurement
The measurement problems arise in part because of an absence of
data for certain variables or for certain countries; different
methods of statistical compilation across countries; and errors
in measurements of the variables.
Composite indices are averages of different sub-indices, and the
single value which they produce may conceal divergencies between
the individual components or sub-indices, possibly hiding useful
information. Furthermore, a composite index implies some form
of trade-off between the sub-indices of the composite index and
averaging would conceal, for example, situations where the effect
of one variable cancels out the effect of another. In addition
there is the problem of whether to take a simple average or a
weighted average and, in the latter case, which weights are to
be assigned to the different variables. In general, the weighting
problem remains in the realm of subjectivity, with the simple
average having a favourable edge on grounds of simplicity.
Some of these problems will probably never be resolved, and their
acceptability and operationality will in the end depend on some
form of consensus.
Professor Lino Briguglio is Director of the Islands and Small States Institute and Head
of the Economics Department at the University of Malta.