Geographical ‘handicaps’ and small states: Some implications for the Pacific from a global perspective

© 2006 Victoria University of Wellington doi: 10.1111/j.1467-8373.2006.00294.x

Asia Pacific Viewpoint, Vol. 47, No. 1, April 2006ISSN 1360-7456, pp79–92

Geographical ‘handicaps’ and small states: Some implications for the Pacific from a global perspective

Harvey Armstrong

*

and Robert Read

†

*Department of Geography, University of Sheffield, Winter Street, Sheffield S10 2TN, UK. E-mail: [email protected]

†Department of Economics, Lancaster University, Bailrigg, Lancaster LA1 4YW, UK.

Abstract:

Research on the economic performance of small states has concentrated on the impli-cations of small size and thin local markets. An interesting feature of many of the world’s smalleststates, of which the Pacific region has many, is that they face additional challenges than just smallsize. Many are remote from global markets for their products. In addition, a large number are alsoislands, many of which are also mountainous. Many of the smaller states are not just islands, butare also archipelagos. Hence in addition to small size, many of the world’s small states also exhibitfour other characteristics that may affect their economic performance: insularity, remoteness, beingarchipelagos and being highly mountainous entities. This paper examines the nature of the chal-lenges posed by these four characteristics and seeks to produce empirical evidence of how difficultit has been to overcome these challenges. The paper draws on empirical evidence for 126 verysmall global states, dependent territories and highly autonomous regions. The paper then turns tothe position of the Pacific small states and dependent territories.

Keywords:

economic performance

,

geographical ‘handicaps’

,

small states

challenges for small states. The paper draws onglobal empirical evidence for 126 entities. By‘small states’ is meant those with a populationof fewer than 5 million. The dependent territo-ries of countries such as the UK and USA havealso been included along with a very smallnumber of subnational regions with unusualautonomy (e.g. Faroe Islands). These entitiesnow have a very high degree of political andeconomic sovereignty and hence closely resem-ble sovereign small states. The paper begins insection 2 with a brief review of the previousliterature concerning the determinants of theeconomic performance of small states, followedby a discussion of the extent to which smallsize, insularity, remoteness, being an archi-pelago and being mountainous represent chal-lenges. In section 3 the methodology used inthe empirical research is presented. In section4 the effects of small size, insularity, remote-ness, being archipelagos and being mountain-ous are examined, using data for 126 globalentities. Section 5 then considers the particularsituation facing the Pacific small states anddependent territories.

Previous research on small states has concen-trated on the implications of small populationsize and thin local markets on how well theyperform (Ward, 1967). Smallness is thought tohave important implications for economic per-formance. An interesting feature of many of theworld’s smallest states, of which the Pacificregion has disproportionately large numbers, isthat they face challenges in addition to smallsize. Many are extremely remote from globalmarkets. In addition, a large number are alsoislands, many of which are also mountainous.Finally, within the Pacific region many of thesmaller states are not just islands, but are alsoarchipelagos, frequently spread across manyhundreds of miles of ocean. Hence in additionto small size, many of the world’s small statesalso exhibit a combination of four othercharacteristics that may affect their economicperformance: insularity, remoteness, beingarchipelagos and being mountainous.

This paper examines the nature of the chal-lenges posed by these four characteristics andseeks to produce some empirical evidence ofhow difficult it has been to overcome these

80

© 2006 Victoria University of Wellington

H. Armstrong and R. Read

Determinants of the economic performance of small states and islands

There is growing evidence that in spite of someserious challenges facing small states, these arenot insurmountable. In fact, the economic per-formance of many, but by no means all, smallstates has been strong whether in terms of theirgrowth rates or income levels (Milner and West-away, 1993; Armstrong and Read, 1995; Milnerand Weyman-Jones, 2003).

The principal

economic challenges

facingsmall states have been analysed at length in theliterature (see Streeten, 1993; Armstrong andRead, 2000 for summaries). Traditionally, themain challenges were seen as

the small sizeof the domestic market

(making economies ofscale difficult to exploit), the

limited

(

and undi-versified

)

resource base

of all but the favouredfew,

over-dependence

on one or two exportproducts and export markets, the need to

import

virtually everything needed for domestic con-sumption (and hence having very little controlover domestic price levels), and

vulnerability

to external economic, political, environmentaland climatic forces (e.g. hurricanes) and otherfactors (e.g. volcanic activity) that can havemassive impacts on their economies (Benedict,1967; Briguglio, 1995; Atkins

et al.

, 2000;Briguglio and Galea, 2003).

How some small states have succeededdespite these challenges is not properly under-stood. Four main reasons have been advancedto explain the successes. The first is that smallstates have had no option but to embrace

open-ness

and export-led growth. This simultaneouslyreduces the impact of the economies of scaleconstraint, and forces firms to be internationallycompetitive. Given their labour supply con-straints, growth in small states is likely tobe founded upon niche markets and humancapital-intensive activities, such as tourism andfinancial services (Armstrong and Read, 1995,2000, 2002a; Armstrong

et al.

, 1998). Not allthe niches are in services (e.g. tourism). Exam-ples exist of successful manufacturing niches(e.g. Mauritius), although manufacturing mayoften be dependent on the service sector ratherthan the other way around (Baldacchino, 1998).Second, successful small states appear to havemade

effective use of a limited policy portfolio

.Because most economic policies (e.g. trade

protectionism, exchange rate manipulation, fis-cal and monetary policies) are ineffective, manysmall states choose to have their exchange ratelinked with a hard currency, and have been ableto negotiate preferential relations with the majortrade blocs and the global economic powers(Kazuku, 1994; Armstrong

et al.

, 1998, 2002b).Third, many small states have been able toexploit their

cultural distinctiveness

and strongs

ocial capital

(Putnam, 1993; Baldacchino,2003), although nepotism and clientilism arealso ever-present dangers. Finally, some smallstates have been lucky enough to inherit valu-able natural resources (e.g. fish, oil) and others,such as those in Europe, have benefited frombeing located in affluent regions of the world(Armstrong

et al.

, 1998; Armstrong and Read,2000, 2001, 2002a).

It must, of course, be constantly borne inmind that by no means are all small statessuccessful. The Pacific certainly has its shareof those facing particularly severe challenges(Ward, 1993). Indeed, some exhibit high out-migration, giving rise to large remittance flows,over-dependence on aid and the kind of exces-sive reliance on ties to the former colonialpowers so characteristic of MIRAB economies(Bertram and Watters, 1985; Bertram, 1986,1999, 2003; Connell, 1988; Aldrich and Con-nell, 1998).

There has been discussion of the extent towhich insularity represents an economic chal-lenge over and above small size (Armstrong

et al.

, 1998; Armstrong and Read 2000,2002a,c). Islands face higher transport costs aswell as other transport-related problems (e.g.unreliability, increased stockholding etc. – Arm-strong

et al.

, 1993). The transport problems haveprobably been exaggerated when one looksacross the full set of global small states, partlybecause transport costs are falling over time,and partly because island economies havesome scope for adapting to these types of prob-lems (e.g. by promoting low-bulk, high-valueexports). On the other hand, transport improve-ments can sometimes exacerbate the challengeswithin islands and within archipelagos (Ward,1993).

Other geographical natural ‘handicaps’ havebeen the subject of much less analysis. Manyisland states are remote from the main globalmarkets of Europe, North America and East

Geographical ‘handicaps’ and small states


81

Asia, particularly those in the Pacific and sub-Saharan Africa. In these cases there will betransport problems and perhaps other difficul-ties (e.g. difficulty of arranging face-to-face con-tacts). Small archipelagic states may also faceadditional problems of domestic market frag-mentation, extra within-state transport costsand weaker social cohesion. Similar argumentsarguably hold true for mountainous entities asarchipelagos since mountains can further frag-ment a small economy.

Finally, there are issues related to the eco-nomic performance of landlocked states. Fortu-nately, none of the Pacific small states fall intothis category, but some of the others in theglobal datasets do. Dummy variables for land-locked states, such as those used in this paper,have proven to be statistically significant ingrowth studies (Barro, 1997; Sachs and Warner,1997; Englebert, 2000).

Methodology and data sources

As noted earlier, a cut-off point of 5 million forpopulation has been adopted in this paper todefine a ‘small state’. The choice of populationas the indicator and 5 million as the thresholdvalue is, of course, to some extent arbitrary andthere has been considerable debate in the pastconcerning definitional matters (see Streeten,1993; Armstrong and Read, 1995; Armstrong

et al.

, 1998). In practice, data constraints greatlylimit one’s choice of an appropriate indicator. A5-million population cut-off point was selected,partly because there is a natural break in thepopulation size series at this point,

1

and partlybecause this point is a popular choice amongother researchers in this field.

Researchers on global small states datasetsface unusually severe data problems. Only avery small number of measures of economicperformance are available, mainly nationalincome values, together with unemploymentrates. The datasets used in this paper, as withprevious research by the authors were assem-bled from the United Nations (UN)

StatisticalYearbook

, World Bank

World DevelopmentReports

, International Monetary Fund

Interna-tional Financial Statistics

, International LabourOrganization statistics, USA

State of the IslandsReports

, the Commonwealth Secretariat’s

SmallStates: Economic Review and Basic Statistics

,

Central Intelligence Agency (CIA) and UK gov-ernment Foreign and Commonwealth Officecountry fact-files, CARICOM datasets and theUN small island developing states (SIDS)datasets. Information was also collected, wherenecessary, directly from the individual states.

Despite the efforts of many small states, threemain data problems remain. First, almost all ofthe main global datasets are

systematically trun-cated

. The smaller the state, the poorer thequality of the statistical information, even fornational income. The result is systematic bias,because it is for the

very smallest

states andislands that data are missing or of poor quality.It is these entities that face the most severeeconomic challenges.

Second, seeking to expand the existingdatasets to overcome the problem of truncationruns into

lack of harmonisation

problems. TheWorld Bank attempts to circumvent this prob-lem by developing

ordinal

datasets, as with itsannual estimates of gross national product(GNP) per capita (now called gross nationalincome (GNI)) for most of the smallest states.Finally, it is usually impossible to construct adataset for a

single year

, even where a singlevariable (e.g. GNI) is concerned. Invariably theresearcher has to be prepared to use data forone or more years earlier or later for some ofthe entities. Fortunately, the relatively stablerankings of economic variables across the dif-ferent micro-states mean that this problem isless severe than it appears.

These problems pose severe challenges forthe researcher. In this, as in previous papers, theauthors have sought to combine the followingapproaches:1

Direct data collection

from state and islandgovernments where data are not available inthe large datasets of supra-national bodiessuch as the World Bank.

2

Robustness checks.

This involves re-runningstatistical analyses with alternative datasets.A fortunate feature of previous research isthat the results do indeed appear to be robust.

3

Using least favourable assumptions

in orderto stack the odds against the hypothesis beingconsidered.

4

Classificatory and limited dependent vari-able techniques.

The authors have electedto use statistical methods designed for non-continuous data, especially

classificatory

82



methods such as discriminant analysis,cluster analysis or principal componentsanalysis, and

limited dependent variable

regression techniques suitable for binary andordinal datasets.

5

Exposing the biases.

This involves identifyingthe biases introduced by data limitations andallowing for these in the interpretation of theresults.

The key dataset used in this paper is that for GNIper capita produced by the World Bank. Statesare grouped into four broad GNI per capitavalues: fewer than $745 per capita, $746–2975,$2976–9205 and more than $9206 ($US). GNIper capita values are also given in purchasingpower parities (PPP) for a subset of these states.Using ordinal data of this kind rather than con-tinuous data means that the degree of truncationis minimised.

As noted earlier, the global dataset has beenextended to incorporate: (i) dependent territo-ries; and (ii) certain highly autonomous regions.In addition, as in previous research, data froma variety of non-harmonised sources have beenused to extend the harmonised datasets of theWorld Bank to a full set of small states to elim-inate truncation. This is a controversial step.Because the additional data are not system-atically harmonised with national incomeaccounting conventions, it is obvious that theauthors’ extended dataset (‘OWNCODE01’) isless reliable than the other data used. Previouswork has shown, however, that most of the verysmallest states do appear to fall very clearly

within one World Bank GNI per capita categoryor another. In addition, as already noted, cross-checks on the robustness of our results havebeen made by re-running models with morereliable (but truncated) datasets.

The effects of size, insularity, remoteness, archipelagos and being mountainous on small states and islands

In Table 1, GNI per capita values based on theWorld Bank’s fourfold categorisation are cross-tabulated against four different categories ofcountry size and (in the final two columns) forisland states as against the full global set ofcountries. Comparing large and small statesfirst, Table 1a shows the results for the GNI percapita values unadjusted for PPP (in $US).Table 1b gives the results (for a smaller set of176 countries) for GNI per capita after PPPadjustment. Four different size categories ofstates are shown: those with fewer than 3million people (‘micro-states’), those with 3–5million people (‘mini-states’), those with 5–10 million people (‘small states’) and those withmore than 10 million people (‘large states’).

Taking the GNI per capita values unadjustedfor PPP first, Table 1a shows that there is ahigher percentage of micro-states in the highestGNI per capita band (38%) than for either mini-states (3–5 million people – 29%), small states(5–10 million people – 22%) or the large stateswith more than 10 million people (18%). Thereverse is true for the lowest per capita GNI

Table 1.

Gross national income (GNI) per capita of micro-states, small states and large states, 2001

GNI percapita ($)

Micro-states

n

(%)

<

3 millionMini-states

n

(%)3–5 million

Small states

n

(%)5–10 million

Large states

n

(%)

>

10 millionAll states

n

(%)Island states

n

(%)

a. GNI per capita, no purchasing power parities adjustment

<

745 11 (15) 7 (33) 16 (48) 30 (39) 64 (31) 9 (16)746–2975 18 (24) 3 (14) 8 (24) 23 (30) 52 (25) 15 (26)2976–9205 17 (23) 5 (24) 2 (6) 10 (13) 34 (17) 11 (19)

>

9206 29 (38) 6 (29) 7 (22) 14 (18) 56 (27) 22 (39)All categories 75 (100.0) 21 (100.0) 33 (100.0) 77 (100.0) 206 (100.0) 57 (100.0)

b. GNI per capita, purchasing power parities-adjusted

<

745 0 (0) 1 (5) 4 (13) 6 (8) 11 (6) 0 (0)746–2975 7 (16) 7 (33) 13 (39) 28 (36) 55 (32) 8 (23)2976–9205 23 (51) 6 (29) 8 (24) 23 (30) 60 (34) 13 (37)

>

9206 15 (33) 7 (33) 8 (24) 20 (26) 50 (28) 14 (40)All categories 45 (100.0) 21 (100.0) 33 (100.0) 77 (100.0) 176 (100.0) 35 (100.0)

Source:

World Bank (2003).



83

category, which contains 39% of the large statesand only 15% of the micro-states. Indeed, themicro-states are found in unusually small pro-portions (compared with either the large statesor all countries taken together) in the lowest twoincome categories (i.e. fewer than $745 and$746–2975). By contrast, they are over-represented in the highest two income catego-ries (i.e. $2976–9205 and more than $9206).There is absolutely no evidence here thereforeof small size being a systematic disadvantage.

‘Islandness’ too would not appear to be pos-ing severe problems because as Table 1a shows,only 16% of island states are to be found in thelowest GNI per capita category and a massive39% in the highest GNI per capita category.Using PPP-adjusted GNI per capita statistics(Table 1b) suggests that these results are robustto PPP adjustment. As would be expected, PPPadjustment tends to shift large numbers of coun-tries out of the lowest GNI per capita category(fewer than $745) because it is the poorestcountries that tend to have the lowest pricelevels. Again, however, it is the micro-states thatare disproportionately over-represented in thetwo highest GNI per capita categories. Some40% of island states fall into the very highestPPP-adjusted category, with no island in thelowest category.

The World Bank datasets are truncated ones,excluding some of the

very smallest

entities. Itis possible that there may be some minimum

threshold level below which small size and/orislandness does indeed become a serious hand-icap and which these datasets are not able topick up. Table 2 therefore presents a similarcross-tabulation exercise to that of Table 1, butthis time concentrating on states with a fewerthan 5 million people. Table 2 identifies a finergradation of states by size and allows a checkmade on whether there is some threshold effectoccurring among the very smallest entities.Table 2a uses the World Bank dataset (for 100small states) whereas Table 2b considers thelarger OWNCODE01 dataset assembled by theauthors (for 127 states) in order to circumventthe truncation effect with the World Bankdatasets. Once again, however, in both cases itcan be readily seen that small size appears tobe no handicap. On the contrary, once againthe evidence suggests that the smaller states,even those with a fewer than 100 000 people,do better than their larger counterparts.

Tables 3 and 4 extend the analysis from coun-try size to island, archipelago, mountainous,landlocked and remoteness characteristics(these variables are described in Appendix I).The first two columns of Table 3 compare theGNI per capita levels of island and non-islandstates with a fewer than 5 million people.Table 3a uses again the 2001 World Bank GNIper capita dataset, whereas Table 3b usesOWNCODE01. Insularity once more does notappear to be a systematic problem because a

Table 2.

Gross national income (GNI) per capita of small states of different sizes, 2001

GNI percapita ($)

Population

<

100 000

n

(%)100 000–

250 000

n

(%)250 000–

500 000

n

(%)500 000–

1 million

n

(%)1–3 million

n

(%)3–5 million

n

(%)All states

n

(%)

a. GNI per capita, World Bank ordinal dataset

<

745 0 (0) 1 (7) 2 (15) 1 (13) 4 (19) 4 (17) 12 (12)746–2975 3 (14) 4 (29) 4 (31) 4 (31) 3 (14) 8 (35) 26 (26)2976–9205 7 (33) 3 (21) 1 (8) 0 (0) 10 (48) 7 (31) 28 (28)

>

9206 11 (52) 6 (43) 6 (46) 3 (38) 4 (19) 4 (17) 34 (34)All categories 21 (100) 14 (100) 13 (100) 8 (100) 21 (100) 23 (100) 100 (100)

b. GNI per capita, OWNCODE01 dataset

<

745 0 (0) 1 (6) 2 (13) 2 (20) 4 (18) 4 (17) 13 (10)746–2975 11 (28) 4 (22) 4 (27) 4 (40) 3 (14) 8 (35) 34 (27)2976–9205 11 (28) 5 (28) 2 (13) 1 (10) 10 (45) 7 (30) 36 (28)

>

9206 17 (44) 8 (44) 7 (47) 3 (30) 5 (23) 4 (17) 44 (35)All categories 39 (100) 18 (100) 15 (100) 10 (100) 22 (100) 23 (100) 127 (100)

Source:

World Bank (2003).

84



smaller percentage of island states (6%) arefound in the lowest GNI per capita band com-pared with non-island states (18%), and ahigher percentage (44% compared with 24%)in the very highest.

A rather more complex picture is revealed bythe contrast between states that are archipela-gos and those that are not. Archipelagos andnon-archipelagos are similarly represented in

the very lowest GNI per capita band (12% each)and archipelagos are slightly over-representedin the highest GNI per capita category (36%compared with 33%). However, closer exami-nation of the middle two per capita GNI bandsreveals a somewhat different picture. Archipel-agos are over-represented in the

lower

middleincome band ($746–2975) and under-represented in the

upper

middle income band

Table 3.

Gross national income (GNI) per capita of islands, archipelagos and mountainous states, 2001

GNI percapita ($)

Islands Notislands

Archipelago Notarchipelago

Landlocked Notlandlocked

Mountainous Notmountainous

a. GNI per capita, World Bank ordinal dataset

<

745 6 18 12 12 19 11 13 11746–2975 20 32 30 24 50 21 26 262976–9205 30 26 21 31 13 31 31 24

>

9206 44 24 36 33 19 37 31 39All categories 100

(

n

=

50)100

(

n

=

50)100

(

n

=

33)100

(

n

=

67)100

(

n

=

16)100

(

n

= 84)100

(n = 62)100

(n = 38)

b. GNI per capita, OWNCODE01 dataset<745 5 17 8 11 19 9 12 8746–2975 24 30 33 23 50 23 22 342976–9205 30 26 21 33 13 31 32 22>9206 41 26 38 21 19 37 34 36All categories 100

(n = 74)100

(n = 53)100

(n = 48)100

(n = 79)100

(n = 16)100

(n = 111)100

(n = 77)100

(n = 50)

Figures are percentages of states.Note: By ‘mountainous’ is meant any state that contains a mountainous area, however small in extent (‘wide definition’ –see Appendix I) as defined by the UNEP-World Conservation Monitoring Centre (i.e. in terms of altitude, slopes and localelevation range).

Table 4. Gross national income (GNI) per capita and the effects of remoteness, 2001

GNI per capita ($) <1000 km 1000–3000 km 3000–5000 km >5000 km All states

a. GNI per capita, World Bank dataset<745 0 0 4 24 12746–2975 0 15 11 32 262976–9205 10 38 27 26 28>9206 90 46 23 18 34All categories 100 (n = 10) 100 (n = 26) 100 (n = 30) 100 (n = 34) 100 (n = 100)

b. GNI per capita, OWNCODE01 dataset<745 0 0 12 20 8746–2975 0 14 32 40 292976–9205 10 38 29 24 27>9206 90 49 26 16 36All categories 100 (n = 10) 100 (n = 37) 100 (n = 34) 100 (n = 46) 100 (n = 127)

Figures are percentages of states.Note: ‘Remoteness’ here is measured as the great circle distance in km between each small state (capital city) and the nearestof the three major global markets – USA (Washington, DC or Los Angeles, whichever is the nearer), Europe (Brussels) andEast Asia (Tokyo).


© 2006 Victoria University of Wellington 85

($2976–9205). The results for archipelagos aretherefore rather ambiguous.

Table 3 also considers the effects of beinglandlocked and being mountainous. Interest-ingly, and in stark contrast to the results forislands, being landlocked does appear to besystematically associated with weaker eco-nomic performance. Landlocked states areover-represented in the two lowest per capitaGNI bands (19% and 50% of landlockedstates, respectively) and under-represented inthe two highest bands (13% and 19%, respec-tively). Finally, Table 3 examines mountainousand non-mountainous states. Being moun-tainous does not seem to cause systematicproblems. Mountainous states are slightly over-represented in the lowest GNI per capita bandand slightly under-represented in very highestper capita GNI band. However, the picture isreversed when the middle two GNI per capitacategories are considered. Mountainous statesare over-represented in the upper middleincome category and have the same proportionof states (26%) in the lower middle incomecategory. As with archipelagos, there is no evi-dence of systematic disadvantage here.

Table 4 turns to the issue of remoteness. Theparticular measure of remoteness adopted hereis the great circle distance from the nearestof the three great global economic hubs(i.e. Japan, USA and European Union (EU)).Appendix II sets out the alternative measuresthat were considered. The measure adopted inTable 4 has been selected solely on the prag-matic grounds that it is the particular remote-ness measure that has proven to be the moststrongly correlated with GNI per capita. Table 4suggests that there appears to be a very strongrelationship between remoteness from the mainglobal markets and GNI per capita for smallstates. Small states that are less than 1000 kmfrom any one of the three main global marketsare greatly under-represented in the lowest GNIper capita bands (no state falls into the verylowest GNI per capita value category) andstrongly over-represented in the highest band(90% of states). Moreover, increasing remote-ness appears to be systematically associatedwith a growing probability of appearing in thelowest GNI per capita bands, with 24% ofstates greater than 5000 km from the nearestglobal market being in the very lowest GNI per

capita value category. As before, repeating theexercise using the authors’ own dataset(Table 4b) does not greatly change the overallresults.

Taking the evidence of Tables 1–4 together itwould appear to be the case that neither smallsize nor insularity leads to systematic poor per-formance in terms of GNI per capita. On thecontrary, the relationships, if anything, suggestjust the opposite. On the other hand, beinglandlocked or being located a long distancefrom the main global markets does seem tohave important implications. The position withrespect to the effects of being an archipelago orbeing mountainous is less clear.

Tables 1–4 represent bivariate analysesbetween each characteristic and GNI per capitain turn. The real situation is, however, essen-tially a multivariate one and it is possible thatsome of the unexpected results may reflect this.For example, if the landlocked states are largelyto be found in remoter parts of the world fromthe main global markets (e.g. sub-SaharanAfrica) then this may explain part of what isgoing on.

In previous research, the authors have soughtto develop an array of multivariate approachesto examine the GNI per capita performance ofsmall states. One method used in previousresearch by the authors, which is crude, butwhich standardises to some degree for ‘remote-ness’, involves comparing GNI or gross domes-tic product (GDP) per capita values for smallstates with the weighted average values of GNIor GDP per capita for contiguous countries orsub-national regions (Armstrong and Read,1995; Armstrong et al., 1998). In this paper, wehave sought to go directly to the heart of thismatter by developing direct distance measuresof remoteness.

Attempts to construct better-specified multi-variate models are severely hampered by: (i)the lack of formal theoretical models; and (ii)the lack of harmonised statistics for most of thepotential explanatory variables. In this paper,we have followed the approach adopted in pre-vious research by the authors by incorporatingexplanatory variables drawn from the researchliterature. Hence the explanatory variablesinclude measures of sectoral structure (i.e. agri-culture – AGRIC, tourism – TOURISM, financialservices – FINAN, and manufacturing –

86 © 2006 Victoria University of Wellington


MANFG), resource endowment (RESOUR),remoteness (DISTGLOBE), sovereignty(SOVRT), ISLAND, archipelagos (ARCHIP),MOUNTAIN and LANDLOCK. Ideally onewould also have wished to incorporate a mea-sure of aid transfers as an explanatory variable.It is, however, impossible to obtain good datafor these, and the presence of aid-in-kind formany dependent territories makes what datathat do exist of limited usefulness. In previouswork on the subset small states for which dataare available, we have sought to incorporate anaid transfer variable and this has proven notto be significant (Armstrong and Read, 2000).Definitions of variables and the alternativemeasures experimented with are discussed inAppendixes I and II.

The grouped nature of the World Bank’s clas-sification of states by GNI per capita makescausal analysis a difficult task. Because thedependent variable is available as the WorldBank’s four per capita GNI classes, a censoredregression model can be used. Of the range oflimited dependent variable models, groupeddata regression is appropriate. Table 5 sets outthe results for two censored (grouped) regres-sion models, the first for the smaller World Bankdataset (‘Model 1’) and the second using the

larger OWNCODE dataset (‘Model 2’). Thedependent variable in both models is GNI percapita whereas the independent variables arethose discussed earlier. In each run of themodel, the column headed ‘Coefficient’ givesthe partial regression coefficients, while ‘t’ is theasymptotic t-ratio.

Checks for multicollinearity showed thatARCHIP and ISLAND face a major multicol-linearity problem, but that the other variablescan be incorporated together. In addition, lin-earity tests indicated that the best results areobtained with DISTGLOBE3 logged. Hence forthe regression analyses separate models are runwith ISLAND and ARCHIP not appearing in thesame model, and with LOGDISTGLOBE3 as theremoteness variable.

Turning to Table 5, it can be seen that AGRICand MANFG have the expected negative andpositive coefficients, respectively, although inMANFG falls just short of the 90% signifi-cance level. The remoteness measure(LOGDISTGLOBE3) is highly significant andnegative as expected. TOURISM, RESOUR andFINAN are statistically significant and with theexpected signs. So too is SOVRT, with a nega-tive partial regression coefficient (confirmingprevious research – Armstrong and Read, 2000;

Table 5. Results of censored regression analysis of the differential economic performance of micro-states – gross national income per capita, 2001

Explanatoryvariables

World Bank dataset Authors’ own dataset – OWNCODE01

Characteristics only:excluding ARCHIP

Characteristics only:excluding ISLAND

Full model:excluding ARCHIP

Full model:excluding ISLAND

Coefficient t Coefficient t Coefficient t Coefficient t

CONSTANT 32414 6.23** 29944 5.70** 32743 6.55** 31562 6.36**AGRIC −2459 −2.69** −2533 −2.65** −1325 −1.57 −1473 −1.75MANFG 1282 1.34 1158 1.21 1796 1.78 1677 1.66LOGDISTGLOBE3 −6433 −4.66** −5353 −3.84** −7592 −5.55** −6898 −5.14**ISLAND 1190 1.25 1110 1.16ARCHIP −1186 −1.32 −737 −0.92MOUNTAIN −628 −0.81 −348 −0.45 420 0.56 590 0.80LANDLOCK −725 −0.65 −1547 −1.45 −1131 −0.91 −1891 −1.61TOURISM 2438 1.85 2958 2.17* 2925 2.75** 3044 2.85**SOVRT −4656 −2.97** −5286 −3.44** 2485 −2.53* −2947 −3.19**RESOUR 4713 3.31** 4211 3.05** 5377 3.85** 4861 3.66**FINAN 1704 1.75 2391 2.63** 1763 1.98* 2098 2.48*Log-likelihood −90.468 −90.367 −125.780 −125.856n 100 100 126 126

Note: The censored (grouped) regressions have been estimated using the LIMDEP software package, using maximumlikelihood procedures. Limits to the groups in these regressions are those of the World Bank’s gross national product per capitaclasses – the dependent variable. A single asterisk denotes 95% significance and a double asterisk 99% significance.



Bertram, 2003). Finally, it can be seen that inno case do ARCHIP, MOUNTAIN or LAND-LOCK appear with significant coefficients ateither the 90% or 95% significance levels.ISLAND too is neither significant, nor does ithave the expected negative partial regressioncoefficient.

These regression results suggest that insular-ity, being an archipelago and being mountain-ous may be challenges, but they do not appearto be systematically causing small states tounderperform. There is a hint that insularitymight even be an advantage, although there isalso a hint that being an archipelago may haveadverse effects, but neither of these are statisti-cally significant results. On the other hand,increasing distance from global markets doesseem to have significant detrimental effects. Theother variables operate much as was found inearlier research of this kind by the authors andhave the appropriate signs.

The situation for the Pacific region’s small states and islands

The statistical importance of remoteness hasdirect implications for the Pacific region’s smallstates and overseas territories. Table 6 sets outthe principal characteristics of the 22 small

Pacific states and overseas territories within thedatabases. As can be seen, the Pacific states aremore likely to be very small, being particularlyover-represented (relative to the full set of smallstates) in the fewer-than-250 000-people cate-gories. In addition, a far higher proportion of thePacific states are islands (100% compared withonly 59% for all small states) and archipelagos(82% compared with 38%). On the other hand,Pacific states are slightly less mountainous (onthe ‘wide definition’ – see Appendix I), althoughsomewhat more mountainous if a stricter ‘nar-row definition’ is used. Finally, the small Pacificstates are distinctly more remote, on average,from the nearest of the three main global mar-kets than are small states in general, with nofewer than 73% of the small Pacific states beingmore than 5000 km from their nearest globalmarket compared with only 37% of small statestaken as a whole. Hence the Pacific states aremore likely to be smaller, islands, archipelagosand remote, and on one of the two measuresare more mountainous too. Indeed, of the var-ious characteristics examined in this paper, it isonly landlocked status that eludes the Pacificstates.

Table 5, however, suggests that only remote-ness and landlocked status appear to besystematic barriers to improved economic per-formance. Of these two, it is remoteness thatposes the greatest challenge for the small Pacificstates. It should come as no surprise, therefore,to discover that the small Pacific states generallydo less well than small states elsewhere. Table 7compares the GNI per capita performance ofthe small Pacific states with their counterpartselsewhere. Interestingly, although there are rel-atively few small Pacific states in the very lowestGNI per capita group (fewer than $745) – only7% compared with 12% for all small statestaken together – the Pacific is disproportionatelyrepresented in the lower middle income cate-gory (47% compared with 26%) and they areunder-represented in the upper middle incomegroup (13% compared with 28%). The less reli-able OWNCODE01 dataset shows similarresults in Table 7.

The relationship between income per capitaand remoteness from global markets is perhapsmost strikingly seen with the CIA’s estimates ofGDP per capita at PPP (a continuous variableunlike the World Bank’s grouped data). Figure 1

Table 6. Characteristics of the small Pacific states and overseas territories

Characteristic Pacific smallstates

All smallstates

Population size<100 000 59 31100 000–250 000 27 15250 000–500 000 5 11500 000–1 000 000 5 81 000 000–3 000 000 0 173 000 000–5 000 000 5 18

Islands 100 59Archipelagos 82 38Mountainous

Wide definition 59 60Narrow definition 50 43

Distance from nearest global market (km)<1000 0 81000–3000 9 293000–5000 18 27>5000 73 37

Figures are percentages.



plots GDP per capita against distance from thenearest global market. The small Pacific statesare shown by black diamonds and the remain-ing global small states as open circles. It can beseen just how clustered the small Pacific statesare towards the far right of the chart (i.e. amongthe most remote of states and also the lowerincome categories).

It would, of course, be dangerous to assumethat remoteness is the sole or even the mostimportant explanation of the economic perfor-mance of the small Pacific states. There areother possible explanations, such as a greaterreliance on agricultural products for export,weak resource endowments for most, the sheernumber of small states in the Pacific (making ithard for individual states to occupy an obviousniche market if another state gets there first) anda dependency culture relying on migrant remit-tances and aid (the MIRAB economies). In addi-tion, it should be noted that only 55% of thesmall Pacific states are sovereign (SOVRT) com-

pared with 69% for small states as a whole.Because SOVRT is negatively associated withGNI per capita, it is clear that the Pacific statesare at an advantage here (Bertram, 2003). Takingall these things together, when as many of theseadditional explanations are allowed for as waspossible (within the multivariate groupedregression model), it is interesting to note thatthe small Pacific states do not stand out as agroup with consistently negative standardisedresiduals in the OWNCODE01 model shown inTable 5 (implying that they have other specialdisadvantages of some sort not included in themodel). Of the 22 small Pacific states, 12 havenegative standardised residuals and 10 havepositive standardised residuals, an almost exactbalance. Moreover, of the 22 residuals only oneis an outlier (with a standardised residualgreater than 3.0) and that has a positive residual.Only one other is greater than 2.0 (i.e. a largeresidual) and that too has a positive value. Whatthe evidence of this paper suggests, however, is

Table 7. Gross national income (GNI) per capita of small Pacific states and overseas territories, 2001

GNI per capita ($US) Pacific states All statesWorld Bank OWNCODE01 World Bank OWNCODE01

<745 7 5 12 10746–2975 47 50 26 272976–9205 13 18 28 28>9206 33 27 34 35

Figures are in percentages.

Figure 1. Gross domestic product (GDP) per capita in Pacific and other small states, 2002Source: CIA country factfiles website.

0

5 000

10 000

15 000

20 000

25 000

30 000

35 000

40 000

45 000

50 000

0 2 000 4 000 6 000 8 000 10 000 12 000

Distance (km)

GD

P p

er c

apit

a ($

US)

Pacific Other states



that small size itself, islandness, being moun-tainous and being archipelagos are almost cer-tainly not serious handicaps for the small Pacificstates. Remoteness, however, almost certainlyis.

Conclusion

This paper has examined the economic perfor-mance of global small states with a populationof fewer than 5 million. A number of conclu-sions would appear to be valid. First, at a globallevel the majority of small states appear toexhibit strong economic performance. Smallsize appears not to be a systematic barrier tohigh GNI per capita. On the contrary, manysmall states, even the very smallest, are shownto perform well.

Second, there is no systematic evidence thatbeing an island state has led to lower-than-expected GNI per capita levels. Indeed, ifanything, insularity is associated with better-than-average levels of GNI per capita. Nor doesthe fact that a small state is an archipelago or ismountainous seem to cause weaker economicperformance. Of all the characteristics exam-ined, only two seem to have systematically neg-ative impacts. The first is remoteness fromglobal markets (i.e. the EU, USA and East Asia).It is this characteristic that appears to be themost serious for the small Pacific states anddependent territories. The second is whether astate is landlocked, with landlocked states hav-ing weaker levels of economic performance.Why landlocked states should do badlywhereas islands do well is unclear. It should benoted, however, that in the full multivariateregression model of Table 5, the inclusion ofother variables (e.g. remoteness) has reducedthe significance of the negative landlockedstate variable dramatically. Part of the negativeimpact of LANDLOCK is, therefore, probablythe result of these states tending to be found ingreater numbers in more remote and less pros-perous parts of the world such as sub-SaharanAfrica.

Acknowledgements

The authors would like to thank the editor forextremely useful comments. Thanks are alsoextended to Geoff Bertram, Godfrey Bal-

dacchino and the other participants of the con-ference Beyond MIRAB: the Political Economyof Small Islands in the 21st Century (VictoriaUniversity of Wellington, 23–25 February2004), and to Jacques Poot for helpful com-ments on an earlier version of this paper. Allremaining errors and misunderstandings are, asalways, the responsibility of the authors.

Note

1 In previous research the authors have used a 3-millioncut-off point, but population growth since then (1994)has shifted the cut-off point upwards.

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Appendix I. The variables used to measure remoteness, being an archipelago and being mountainous

A number of different measures were experi-mented with prior to the final selection.1 Remoteness

Estimates of great circle distances (km) fromthe capital city of each small state were basedon latitude and longitude. Four variableswere developed: (i) distance to the EU (mea-sured at Brussels); (ii) distance to the nearestof the three main core areas of the globaleconomy, namely the EU (Brussels), the USA(Washington, DC or Los Angeles, whicheveris nearer) and East Asia (Tokyo); (iii) distanceto the capital city of the major economicpower of nearest global region (e.g. Pretoriafor southern Africa), the World Bank’s globalregions being used throughout; and (iv) dis-tance to the nearest large city outside thesmall state (e.g. Auckland for many Pacificstates). Measure (ii), DISTGLOBE3, had thegreatest explanatory power, estimated interms of correlation with GNI per capitavalues and hence is the variable used in theregressions. Linearity tests also showed thatlog-distance performed better than a simplelinear version.

2 Archipelago statesA simple binary variable was used in theregressions for this variable, based onwhether the state had one or more than oneinhabited island. Two alternative measureswere also developed: (i) the percentage ofpopulation located on the main island (notnecessarily where the capital city is; the datafor this were pieced together from the web-sites of the 127 entities and are of very vari-able quality); and (ii) the percentage of thestate’s population located in the capital cityor town. This too had to be based on data ofvery variable quality and has the added dis-advantages of being influenced by differentboundary definitions for the cities and by thefact that in some states the political capitalis different from, and much smaller than,the main economic centre(s). Interestingly,although it was expected (based on the trans-port cost debate) that the variable that mea-sured the percentage of the populationconcentrated on the main island would per-



form better than the other two measures, thisdid not prove to be the case and the simplebinary variable was preferred. It was also dis-covered in the multicollinearity tests thatARCHIP was closely correlated with ISLAND(r = 0.62). Interestingly, LANDLOCK is notstrongly correlated with ISLAND (r = 0.45).

3 Mountainous statesThe definition of what constitutes a moun-tainous region or state remains highly conten-tious. In this paper, two very crude measureswere developed, drawing upon maps con-structed from remote sensing data by theUnited Nations Environment Programme(UNEP) World Conservation Centre andavailable on the Web. These identify moun-tainous areas using a combination of altitude,slopes and elevation range. The ‘widestdefinition’ category incorporates altitudes>300 m, slopes ≥2° and an elevation range of>300 m. The UNEP maps are excellent andoffer a great opportunity to take forward mea-sures of this kind, but at the time of theresearch only large-scale maps were avail-able and no separate statistics of areas thatcan be classified as mountainous for eachsmall state in turn were available. The highlyunsatisfactory method of ‘eyeballing’ themaps to ascertain rough percentages of eachstate’s land area that was mountainous had tobe used. To reduce error, two very simplebinary variables were developed. The first, or‘wide definition’ for MOUNTAIN classified asmall state as mountainous if it contained anyof the UNEP’s ‘mountainous areas’, howeversmall, and using UNEP’s own widest defini-tion of areas that are mountainous. Thisturned out to be the MOUNTAIN variable,which produced the best results and hence isreported in the paper. A second variable wasagain a binary variable, this time picking outsmall states where the mountainous areaexceeded 50% of the total area of the state.Given the need to eyeball maps, this variablemust be regarded as particularly unsatisfac-tory and this gave a second reason why themore accurate MOUNTAIN binary variablewas preferred. In fact, neither of these vari-ables is ideal for picking up likely influencesof ‘mountainous’ on transport costs andissues such as local market fragmentation.Ideally, one would probably wish to develop

within-state gravity model accessibilityindexes, which could combine the effects ofboth mountain terrain and being an archi-pelago. Such variables simply do not exist atthe present time.

Appendix II. The variables used in the censored regression

There are seven explanatory variables for whichit has been possible to develop reasonably com-prehensive international statistics for the smallstates:1 ISLAND is a binary variable indicating

whether the state or dependent territory is anisland (or archipelago) or not. The a prioriexpectation is for a negative relationship withGNP per capita.

2 The next five variables together represent‘industry structure’ explanations of the rea-sons for the economic performance of differ-ent small states. Each of these – TOURISM,AGRIC, RESOUR, FINAN and MANFG – is asector picked out in the small states literatureas likely to have an important influence oneconomic performance. The TOURISM vari-able is a binary variable indicating whether astate or dependent territory has fewer (0) tour-ists per 1000 population than the mean value(using UN and World Tourist Organizationdata, combined with information collecteddirectly from the micro-states).

3 The FINAN variable is a binary variable indi-cating the presence or absence of a majorfinancial services sector in a small state ordependent territory. This variable has reliedon an unusual amount of judgement by theauthors because comprehensive data atworld level simply do not exist and the pres-ence of institutions or appropriate regulationsto attract financial services does not guaran-tee the actual presence of a large sector. Var-ious sources, including Tolley’s Tax Havens(Croydon, UK: Butterworth Tolley) and R.Roberts, Offshore Financial Centres (1994)(London: Edward Elgar) were used to con-struct this variable.

4 MNFG attempts to pick up the influence of astrong manufacturing export sector on smallstate economic performance. Successfulsmall states (e.g. Mauritius) frequently havehigh value-added manufacturing sectors tar-



geting key export markets. A ‘1’ value on thisbinary variable indicates a greater-than-average share of manufacturing in GDP (usingWorld Bank and CIA Factfile data togetherwith data from individual states) and a ‘0’value indicates a less-than-average share ofGDP in manufacturing. This variable shouldbe positively related to GNP per capita.

5 AGRIC is included because many developingmicro-states are heavily dependent on agri-cultural products for export or domestic con-sumption. The variable is again a binary one,indicating whether or not agriculture formsa higher percentage of GDP than the worldaverage (using World Bank and CIA fact-filedata). Although one or two micro-states pro-duce high-value agricultural products forexports, most of those dependent on the agri-cultural sector in the developing world areeither producing large amounts of productsfor local consumption or are producing prod-ucts such as bananas that have low prices in

export markets. It is expected, therefore, thatthis variable will be negatively related toGNP per capita. Correlation tests revealedthat there is no significant multicollinearitybetween AGRIC, TOURISM and MNFG.

6 RESOUR is included in order to try to pickup those cases where a micro-state ordependent territory is fortunate enough tobe endowed with a high-value resource (e.g.Iceland and its fisheries). Once again, a vari-ety of sources have had to be drawn uponto construct this binary variable, includingOrganisation for Economic Co-operationand Development data on oil and fishresources, together with specific countrydata for other resources (e.g. phosphates).This variable should exhibit a positive rela-tionship with GNP per capita.

7 SOVRT is a binary variable indicatingwhether, using the UN definition, the entityis a sovereign micro-state (a ‘1’ value) or adependent territory (a ‘0’ value).

Documents

Geographical ‘handicaps’ and small states: Some implications for the Pacific from a global perspective