A Comparative Analysis of the Economic Performance of Greek and British Small Islands
Harvey Armstrong, Dimitris Ballas and Adreene Staines
University of Sheffield
Paper presented at the 36th Regional Science Association International (British and Irish Section) conference, Jersey, Channel Islands, 16-18 August 2006.
Draft: Please Do Not Quote
Correspondence: Department of Geography, The University of Sheffield, Winter Street, Sheffield S10 2TN, UK. Tel: +44 114 222 7906. Fax: +44 114 279 7912. E-mail: [email protected], [email protected], [email protected].
Abstract
There has been a growing interest in recent years in the nature of the economic challenges facing island economies, and in the determinants of differences between islands in their relative economic performance. In an EU context, this has led to special status being granted within the EU for particular types of island economies (e.g. the Outermost Regions, six of the seven being islands, and for wider groups of islands for the 2007-2013 Cohesion policy programmes). Research on island economies within the EU is severely hampered by poorly harmonised statistics within the main Eurostat data sets. This paper concentrates on two EU member states (Greece and Britain) which have large numbers of island economies, many of which are in highly peripheral locations with respect to the main EU markets and frequently simultaneously having other ‘geographical handicaps’ (e.g. mountainous, comprising archipelagos etc). National as well as EU level data are analysed to produce typologies of islands in the two member states. These typologies are utilised to identify similarities and differences between British and Greek small islands and to speculate on possible causes of economic performance variations within and between the islands of these two countries.
1 A Comparative Analysis of the Economic Performance of Greek and British Small Islands
1. Introduction
This paper presents preliminary results of an analysis of the relative economic performance of two different sets of EU island economies. The first comprises some 60 offshore British small islands, whilst the second comprises 63 offshore Greek islands. The two countries were selected partly because there are some close similarities (particularly in respect of remoteness from EU markets), partly because the two countries contain within them large numbers of islands, and partly because there are some important and interesting differences with respect to geographical characteristics of the islands which might affect economic performance.
The paper draws mainly upon 2001 data from the British and Greek population censuses, supplemented with data from EU sources. Separate cluster analyses are conducted on the Greek and British islands data sets.
The paper begins (section 2) with a review of the existing literature on the economic performance of island economies, focusing in particular on the role which geographical characteristics might play. Section 3 examines the data sets used and the method of analysis adopted (a Ward’s method cluster analysis). The results of the cluster analysis are set out in section 4, and similarities and differences between the Greek and British clusters discussed. The conclusion (section 5) summarises the results and speculates on future research.
2. The Economic Performance of Islands: Is Insularity an Advantage or Disadvantage?
2.1 Islands as a special focus for policymaking
Islands, and in particular small islands, have long been thought to face a particularly distinctive set of challenges likely to hinder their economic performance. As shall be shown, the perception that islands are at some sort of inherent disadvantage when compared to non-island (mainland) economies is deep-seated, so intuitive in nature as to be almost a ‘gut reaction’ for many people, and extremely persistent in nature. At the present time, this perception is vividly manifested in the European Commission’s view of insularity as a geographical ‘handicap’, one of several such handicaps requiring remedial policy action:
“Regions with specific and permanent geographical features which constrain their development, such as the most remote regions, islands, mountain regions and sparsely populated areas in the far north of Europe, have special problems……. All of these regions, in
2 whichever part of the EU they are located, have common problems of accessibility and of remoteness from major markets which tend to add to both travel and transportation costs and constrain their economic development” European Commission, 2004, p.30 and p.33.
The EU is not alone in taking the view that insularity is a ‘handicap’ requiring special policy treatment. The UN officially recognises a category of small island developing states (SIDS) as being member states with distinctive economic development problems, and the British Commonwealth too has recognised its small island states as being distinctively vulnerable economic entities. In addition, many individual countries, both in the EU and elsewhere around the world have developed favourable policies and governance structures for many of their offshore islands (e.g. Corsica in France, the Faröe Islands of Denmark and the Åland Islands of Finland).
If one examines the academic and policy literature on island economies, it rapidly becomes apparent that it is the combination of two geographical characteristics, small size and insularity, which is seen by most as being the root cause of the perceived ‘handicap’. Moreover, most authors seem to regard the ‘islandness’ part of this double handicap as being fundamentally the result of transport problems, while the ‘smallness’ handicap is usually seen as being the result of the island companies being unable to reach critical minimum production scale levels.
This rather traditional view of insularity being a handicap for economic performance has been revealed by research in recent decades to be a considerable over-simplification of the economic challenges faced by small island economies. The reality is more complex. Moreover, there is now considerable evidence that many small islands have been able to produce economic growth performances and standards of living for their citizens which can be at least as good, and often better than their larger, mainland counterparts. In the remainder of this section, three very different literatures which have addressed the economic performance of small island economies are reviewed.
2.2 The evidence from global small states research
There is a large and growing literature on the economic performance of global small states which has thrown considerable light on the nature of the challenges faced by small island economies. A high proportion of the smallest countries in the world are island (or archipelagic) economies. If one adds to the sovereign states with UN membership those entities which are not fully politically sovereign but which have a very high degree of both political and
3 economic autonomy1, one finds that of 127 small states and highly autonomous entities with populations under 5 million persons, no fewer than 74 of these are islands or archipelagos (Armstrong and Read, 2005). As shall be shown, most of the small states research literature is highly relevant for islands all around the world, irrespective of whether they are sovereign states or not.
With so many small island states in the world, it is not surprising that they have attracted particular research attention. The small states literature has identified a whole series of different types of economic challenges facing island economies. The principal ones are as follows:
(a) Small population size, coupled with greater difficulty in gaining access to wider regional and global markets because they are islands, means that the domestic market may be too small for local businesses to attain minimum efficient scale (MES – Bhaduri et al, 1982; Kuznets, 1960). This has two rather differing implications for economic performance. Firstly, the local businesses will find it difficult or impossible to compete in wider regional and global markets to win exports, and secondly, to the extent that the businesses seek to serve the local market, local prices will be higher, raising the cost of living for island residents. The MES argument was traditionally couched in manufacturing industry terms since it is in manufacturing that scale economies are most pronounced. The argument is also, however, valid for some service sector industries (e.g. banking and finance) and, perhaps more importantly, for the major utilities sectors (e.g. water, electricity, telecommunications). Below-MES production levels in the key utilities has a double- barreled effect; it raises the on-costs for other businesses (hence reducing further their competitiveness in external markets) and it raises the direct costs of utilities to local households, increasing island cost of living.
(b) Closely related to the MES argument is the implications a small domestic market has on the competitive environment within-island. Not only may the firms which exist be too small to be efficient, but the small market is also likely to mean that developing a critical mass of competing firms becomes impossible. This can have different ramifications. The most obvious and direct effect is that there will be many sectors in which local monopoly or very limited local oligopoly situations exist, raising prices and costs right across the island economy (Armstrong et al, 1993). Less direct, but perhaps of greater long run importance, the strict limits on the numbers of local businesses in a given sector means that the
1 These comprise the remaining colonial entities such as the UK’s overseas territories, the French Territoires d’Outre Mer (TOMs), the USA associated territores, and other similar entities for the Netherlands, Australia and
4 potential for vibrant industrial clusters to emerge is severely curtailed. There is now a large body of evidence of the importance of industrial clusters for the economic development of regional economies, and new economic geography (NEG) theories, of course, place enormous emphasis on the ability of clusters to exploit external economies and agglomeration benefits. In recent years it has become apparent that it is possible for some islands to be able to develop successful industrial clusters. This can be done by developing cross-border networks with other islands or mainland regions (as Singapore has done with adjacent parts of Malaysia and Indonesia). In other cases it has been achieved by overcoming the barriers of distance by exploiting family and cultural ties (as between Mauritius and India in the textile industry). In practice, however, most islands have not been able to develop industrial clusters in this way and have therefore failed to exploit the advantages of industrial clusters within the modern global economy.
(c) Some of the most severe challenges faced by small island economies are the result of factor supply (or ‘resource base’) limitations. These frequently apply to the full set of factors of production (i.e. land, labour, capital and natural resources). Small population not only limits the size of the local market for goods and services, but also places strict limits on the local labour supply. This can, of course be supplemented by in-migration and many islands do indeed seek to attract both seasonal and permanent migrants, but in practice there are limits (geographical, political and social) on how far this can be taken. A small local labour force has a series of implications for economic performance. It places constraints on the agricultural sector. Moreover, creating a manufacturing base in a labour-intensive manner (as is common in many developing countries) cannot be achieved in small island economies (Bhaduri et al, 1985). In addition, as has already been noted, a small population size means that the entrepreneurship base will also be small, with only small numbers of firms being created. Islands typically respond to the labour force constraint by concentrating their limited resources on highly specialised niche market exports. In some cases the islands can end up being highly dependent on a single industry for their exports (e.g. banana production, oil or fish exports for islands lucky enough to have a natural resource endowment, or only one or two manufacturing or service sectors, as with the many offshore finance based island economies, or those wholly dependent on tourism). The resulting lack of diversification is thought to have two rather different effects on island economies. Firstly, over-specialisation combined with an extremely small share of external export markets makes many island economies classic price-takers in export markets. This inability to significantly influence external market conditions makes them very vulnerable
New Zealand, together with semi-sovereign entities such as the Faröe Islands, Åland Islands etc. 5 to sudden changes in trading conditions. Sudden trade shocks are particularly serious because with a high proportion of factor resources are tied up in the main export sector there is little in the way of a non-export sector to absorb changes induced by sudden switches in external trading conditions. Secondly, the high degree of specialisation has longer-term implications as there is unlikely to be cohorts of small firms in other sectors waiting to come on-stream as traditional staple sectors go into long-term decline. In an economy such as the Isle of Man in the 1980s and 1990s, faced with a steady decline in its traditional summer holiday tourist market as UK travellers moved elsewhere, finding alternative sectors into which resources could shift was no easy task.
(d) The small states literature has also begun to throw more light on the challenges posed by higher transport costs for island economies. Of all of the ‘handicaps’ faced by small islands, it is transport costs which are the most obvious problem. At its simplest, the transport cost issue for islands is about transhipment costs. Islands, simply because they are islands, face two additional sets of transhipment costs (loading and unloading) which mainland regions do not. Transhipment costs affect both freight and passenger transport, involve more than just the costs of physical movement (e.g. they also include paperwork and bureaucracy costs), and are known to be high relative to the line-haul element of total transport costs. It is, however, important not to be too simplistic about the nature of transhipment costs. To begin with, new vehicle and port technologies (especially roll on-roll off ferries and containerisation) have greatly reduced transhipment costs over time, just as larger maritime, air and road transport vehicles (able to exploit vehicle economies of scale) and the growth of route densities and trip frequencies as trade has expanded have cut other elements in the transport cost package. It is clear therefore that the transport costs ‘handicap’ facing small islands has been falling over time, allowing many islands to become more intergrated with the regional and global economy. In addition, the steady build up of investment in ports, airports, roads and other elements of transport infrastructures, particularly in the EU where large amounts of Structural Funds, TENs and other public policy investment spending have occurred, has also helped to reduce the burden of transport costs for islands. Nevertherless, it is clear that islands continue to face a burden of additional transport costs, even though these may be falling over time. Being an island economy brings with it an array of other transport-related problems which are only now becoming fully understood. These include:
• Freight insurance costs and damage-in-transit costs are higher for islands relying on maritime and air transport links and with transhipment activities being a necessary part of the equation. In addition, islands are more likely to face greater uncertainty of
6 services given the risks of bad weather and mechanical disruption. The latter is diminishing in effect as transport technology improves over time, but these factors remain a sufficient threat to cause additional business costs (especially in the form of higher inventory holdings).
• The volumes of import and export freight and passenger flows frequently mean that neither vehicle nor port economies of scale (which are very significant in all three transport sectors – sea, road and air) can be fully exploited.
• The small scale of import and export flows usually mean that origin and destination ports are extremely few in number (sometimes involving only a single sea route). This raises the costs of transport and business costs on the island in two ways. Firstly, local monopolies are very frequent within the freight and passenger transport sectors, with implications for higher costs and price levels. Secondly, island exporters and importers have few route choice options, often leading to longer and less direct shipment routings, with additional cost implications.
• A rarely recognised but important problem faced by many islands is the asymmetric nature of freight flows. Island consumers naturally demand a full array of consumer products. These are typically higher bulk and lower value than the export freight flows (since island exporters seek to overcome the geographical barriers by concentrating on high value, low bulk products, or else on services such as financial services with negligible freight flows). This means that import volumes tend to be much higher than export volumes. With many vehicles returning empty or at less than 100% loads, transport costs are effectively doubled for many islands.
(e) Finally, there is a substantial literature on the vulnerability of small island states. One element of the greater unpredictability of conditions facing small islands has already been touched upon, namely the fact that islands are usually price-takers in external markets, making them vulnerable to sudden shifts in terms of trade and other external economic factors. They are also highly specialised exporters, frequently dependent not only on the export earnings from a single or small number of products, but also disproportionately dependent on a single overseas market (often the former colonial power in the case of sovereign island small states – Bertram, 2003). Economic vulnerability is, however, only one of a series of vulnerabilities which can affect the economic performance of islands (Atkins et al, 2000; Briguglio and Galea, 2003). Islands face environmental vulnerabilities (e.g. hurricane damage, crop pests, earthquakes), including the severe impacts in some cases of volcanic eruptions. Many small island states are also politically vulnerable, for two
7 reasons. Firstly, they are often highly dependent on a single large country (usually an adjacent large state or the former colonial power) for political influence in international negotiations. Secondly, their small size means that they have little influence in wider international debates and decisions, often being wholly unrepresented in bilateral and multinational negotiations and with very limited consular presence in many parts of the world. The various different elements which make up the overall picture of vulnerability combine to make island states more likely to have volatile incomes and consumption levels over time, interspersed with massive and sudden shifts at rarer intervals.
The list of challenges set out above facing small island states is a long one. Closer scrutiny of the different ‘handicaps’ shows that virtually all of them apply not only to sovereign small states that happen to be islands but also to sub-national islands which are not sovereign states. Although there has been much less research on non-sovereign islands than there has been on small sovereign island states, it is clear that virtually all of the same arguments apply. The implications of the small states literature for the analysis conducted in this paper are two-fold. Firstly, the Greek and British small islands analysed in this paper almost certainly face the full set of economic challenges set out above. Secondly, the impact of each challenge in turn will differ from island to island, and hence the challenges set out above may well be important determinants of the relative economic performance of the different islands. For example, those islands with good port and airport infrastructures will face smaller transport costs and less disruption to services than those without. Similarly, bigger islands are more likely to be able to exploit transport economies of scale than smaller islands, and so on.
There is a further part of the small states literature which has important implications for the Greek and British islands studied in this paper. The small states literature has devoted considerable time in recent years to the analysis of the policy responses to the economic challenges faced by islands. The reason for the focus on policy responses is because there is now considerable evidence that the economies of small island states do not do uniformly badly as the long list of ‘handicaps’ would suggest. On the contrary, many small island states have performed spectacularly well (e.g. Singapore, Bermuda). Moreover, systematic studies of large data sets comprising large and small states have revealed that neither population size nor ‘islandness’ seem to be systematically related to poor economic performance (Armstrong et al, 1998; Armstrong and Read 2000, 2003a, 2003b; Bertarm and Karagediki, 2004). In other words, since islands do seem to face some distinctive problems, many of them must have found policy responses which have allowed them to overcome their ‘handicaps’. The main advantages they seem to have been able to exploit have been as follows:
8 (a) Although it is conventional economic policy logic that industrial diversification is a good policy stance, it is clear that many small island states have been able to make a virtue out of a necessity by having a clear niche market strategy. Focusing on high earning niche markets such as cruise tourism or offshore financial services may be a risky strategy and poses vulnerability threats, but it is nevertheless still possible to have a high standard of living whilst the good times last. The keys to success for many of the better-performing small island states seem to be to (a) focusing government policy support on the key export earning sector whilst the going is good (e.g. transport infrastructure and hotel/leisure investment for tourism; business regulation manipulation for offshore finance and other services), combined with (b) rapid and flexible policy responses when it becomes necessary to abandon one niche market and move to another (e.g. the response of the Channel Islands to the loss of UK early vegetable and flower markets following EU entry by the UK).
(b) Many islands have made a virtue out of necessity in another way too. Since small islands are inherently price-takers in external markets, and have very little influence on global trading conditions and regimes, they have by necessity had to adopt highly open trading policies. Trade restrictions are only very rarely imposed since virtually all of the locally consumed goods must be imported and since exports are the life blood of all islands. Indeed, many small island states do not even have their own currency (adopting the currency of a larger nearby state or the US dollar or Euro, or else have rigidly locked exchange rates with a larger country currency such as the dollar). Since small island states have by necessity always been highly open, trading economies, it can be argued that they were fortunate in avoiding the pitfalls of protectionism, a policy stance which bedevilled many newly decolonised developing countries from the 1960s onwards (Chai, 1998). Most small island states were therefore in the fortuitous position of already having highly open trade policies in place when globalisation began to take off in the 1980s and 1990s and were therefore able to benefit quickly from the new market opportunities opening up.
(c) Small island states, like other non-island small states, have also undoubtedly been able to exploit a whole series of policy options based upon what has come to be known as the ‘importance of being unimportant’ (Armstrong and Read, 2002). This is most vividly seen in the manner in which many small island states have been able to develop highly successful offshore financial centres (Cobb, 2001; Hampton and Abbott, 1999). These invariably rest upon the establishment of flexible banking and financial regulations which are tolerated by the large states of the world and by entities such as the EU and OECD simply because the scale of financial activities in the offshore centres is too small to trigger
9 retaliatory action. The ‘importance of being unimportant’ policy loophole is not confined to financial services. Many small island states also adopt flexible business start-up and bankruptcy regulations, environmental regulations and maritime industry regulations, allowing yet more niche sector activities to be established.
(d) Somewhat more controversial than the ‘importance of being unimportant’ argument is the view that small island states may have better social capital (Putnam et al, 1993) than large states. The argument here is that small island states have more homogeneous communities and are small enough to allow policy decisions to be quickly implemented and in an atmosphere of greater transparency and trust, important building blocks for good social capital. This argument may well be a good one for some small island states. However, ‘smallness’ may not always be a virtue for social capital accumulation since it is also evident that nepotism and clientalism are rife in some island states. Indeed, it is often possible for small cliques of business leaders to come to dominate an island’s political life, to the detriment of open trading and a welcoming business environment.
(e) Many small island states have retained close economic and political ties with their former colonial powers, despite their own sovereign independence (Bertram, 2003). These continuing close ties often have resulted in substantial bilateral aid flows (usually financial aid but also often aid-in-kind through the provision of teachers, civil servants etc). The former colonial powers will also often use their influence in multilateral negotiations to obtain preferential trade access to trade blocs such as the EU or in WTO agreements.They also often use their influence to generate additional aid from large multilateral organisations such as the World Bank and the UN.
(f) ‘Islandness’ is not always an inherent disadvantage in itself. For example, being an island is often in itself a major attraction for tourists, even if the climate is poor.
An interesting issue which has not yet been fully analysed within the small island states literature is why some small islands have done spectacularly well whilst others have done extremely badly. Small island states are to be found at both extremes of the spectrum of economic performance in the global economy. What research has been done suggest that once again it seems to be the policy response which may be the explanation. The literature suggests that it is possible for many developing small island states to retain an economic dependency culture once they have obtained political independence from colonial powers. The evidence suggests that there is quite a large group of small island states that appear to have developed a stable, but low level of economic performance based upon income from migrant remittances (usually from migrants to the former colonial power), aid (in the form of financial flows and
10 investment in public infrastructure, again often mainly from the former colonial power) and bureaucracy (hence ‘MIRAB’ economies – Bertram and Watters, 1985). It is recognised that there may be several different variants of the MIRAB model, but it is an accurate summary of one particular type of policy response by some small island states. In contrast to the MIRAB economies are those small island states which have deliberately made major policy efforts to break free of dependency situations. In a recent paper, Bertram (2006) argues that there may be two distinctive types of successful small island states. The first are so-called PROFIT economies (with success based on successful labour and residential management – ‘people’, natural resources management, overseas engagement, financial services and transportation management – Baldacchino, 2006). The second are the so-called SITE economies (small island tourism economies, which are much more specialised but also successful). Whatever the realities of the situation, the key finding of this part of the literature is a clear one – it is the policy response which matters and good policies can lead to success, despite the handicaps facing small island states.
The ‘policy response’ part of the small states literature is less directly applicable to Greek and UK islands than is the ‘handicaps’ part. Sovereign island states have a much greater degree of control over the policy levers than do Greek and British small islands whose local government policy powers are both limited and virtually identical to those of their mainland counterparts. It is true that sovereign small island states normally have very little in the way of macroeconomic policy powers (with limited fiscal policy power and usually no monetary policy powers). Trade policies too are usually non-existent since as noted earlier small states must by necessity adopt free trade policies (Read, 2002). In these respects they are almost identical to the Greek and British islands which are the subject matter of this paper. By contrast, however, sovereign small states have considerable microeconomic management policy powers. Unlike the Greek and British islands, they can manage residence and seasonal/temporary migration flows in order to sustain economic activity and keep local unemployment rates low. They also have major powers over personal and business tax regimes, often used to attract high income residents and investors from overseas. Moreover, as has already been noted, they can manipulate their ‘importance of being unimportant’ to set in place highly attractive financial, business, maritime and environmental regulations designed to foster strong niche market sectors. The Greek and British islands which are the subject matter of this paper have none of these powers. Indeed, their respective national legislation frameworks, combined with strict EU Single Market prohibitions on barriers to labour and capital mobility and the free movement of goods and services explicitly rule out most of the microeconomic policies enjoyed by sovereign small states.
11 We may therefore conclude that whilst the Greek and British islands which are the subject of this paper face virtually all of the economic challenges posed by ‘islandness’ as their sovereign island state counterparts, they lack many of the distinctive policy powers enjoyed by sovereign small island states. One might therefore speculate that the Greek and UK small islands face a harder struggle to succeed than their nearby sovereign counterparts (e.g. Cyprus and Malta in the case of Greece and the Isle of Man and Channel Islands in the case of Britain). Indeed, probably the sole advantage which the sub-national islands enjoy over their sovereign counterparts is that they have more direct access to aid from their national finance ministries (as well as EU structural funds and agriculture policy subsidies). There is no doubt that many Greek and British islands do benefit from intra-national fiscal transfers and direct aid (often for transport infrastructure), but this is probably a poor substitute to the kind of microeconomic policy weapons available to sovereign small island states.
2.3 The evidence from international growth model research
A second literature which has thrown light on the effects of geographical ‘handicaps’ (including insularity) on economic performance has been attempts to model differences in economic growth rates between different countries of the world. There has been a great flowering of studies of this kind since the early 1990s, in recent years much of it being driven by a desire to understand why some global regions have performed more poorly than others (especially sub-Saharan Africa). The development of large international data sets has also meant that the study of international growth differences has been used as a laboratory to test different types of economic growth theories.
International growth models do, however, only occasionally include ‘islandness’ as one of the (usually many) explanatory variables tested. This is principally because an unfortunate feature of the large data sets used is that they are highly truncated. They are truncated two key respects, both of which render their findings of only very limited use for those interested in the economic performance of small island economies. Firstly, data limitations mean that many of the very smallest sovereign states are excluded. Although most small sovereign states have in recent years greatly improved their statistical data bases, especially for the national income accounting data so vital for growth model research, the smallest states lack the long time series for key variables which exist for large states. Secondly, because so many of the world’s smallest states are islands (or archipelagos), the main data sets are also inherently highly truncated in that they exclude many island economies.
12 Despite the highly flawed nature of the data sets used for international growth analysis, at least as far as those interested in island economies are concerned, the international growth model research literature has thrown some interesting light on the nature of the issues of concern in this paper:
• Those few studies which do incorporate size and/or insularity explanatory variables (size usually being measured as population size and insularity normally a simple dummy variable) have tended to find that these variables are statistically insignificant (Milner and Westaway, 1993; Armstrong and Read, 2003b). It therefore does not seem to be the case that small size or insularity have systematic effects on national economic growth rates, despite the many challenges such states face (see above). In this respect the growth literature supports the evidence set out earlier from the small states research literature, and presumably for the same reasons. Again, however, it must be stressed that since the main data sets exclude many of the very smallest island states it is possible that significant relationships might exist had the data sets used been more comprehensive and had not excluded the tail of very smallest island states.
• Whilst size and insularity do not figure prominently in the growth model research literature, there are a number of other ‘geographical handicap’ variables which have proved to be statistically significantly related to variations in economic growth rates. In fact, quite a wide array of different ‘geographical’ variables have been tested. These include accessibility/remoteness from global markets, whether a country is landlocked or not, climate (tropical climate/disease), how mountainous a country is, and size. Unfortunately, those geographical variables which have been shown to be statistically significant in growth models (especially tropical climate, remoteness and landlocked status) are also highly correlated with a number of institutional variables (e.g. size of government, corruption, civil strife etc). This has led to the still-unresolved issue of whether it is ‘geography’ or ‘institutions’ (or both) which are the principal determinants of international differences in economic growth (Sachs, 2003; Ahlfeld et al, 2005; Sachs and Warner, 1997).
It remains impossible at present to be sure from the international economic growth research of just how important ‘geographical handicap’ variables in general and ‘islandness’ in particular are in determining international economic growth rates. However, what can be said at the present time is that (a) what international growth evidence exists does tend to support, despite its flaws, the evidence from small states research that small size and islandness does not necessarily result in weaker growth, and (b) if geographical variables are important in
13 determining economic growth differences, then of those which have been found to be most significant to date (i.e. tropical climate, landlocked status, remoteness), it is only ‘remoteness’ which is relevant for the Greek and British islands which are the focus of this paper (since neither tropical climate nor landlocked status apply in our data sets). As shall be shown later, we have attempted to incorporate accessibility measures into the analysis undertaken.
2.4 The European Commission’s view of island ‘handicaps’
The quotation from the European Commission with which this section began clearly identified insularity as being a characteristic which it sees as acting as a constraint on economic development, and the nature of the constraint is closely linked to transportation (in particular via difficulties in accessing the wider EU market). This view represents a highly traditional one of island economies. As such, it is rather an old fashioned one because as we have seen in sections 2.2 and 2.3 (above), recent international research has revealed a much more complex picture of many different ways in which ‘islandness’ affects economic performance, by no means all of which are negative.
The quotation does not, however, do complete justice to the Commission’s view of the nature of the ‘handicap’ associated with insularity. The Commission has had long experience in developing policies (particularly Structural Funds, agriculture and transportation policies) for islands, simply because the EU has so many islands2. A major study undertaken recently on behalf of the Commission found, using a narrow definition of what constitutes an ‘island’3, that there were no fewer than 286 island territories within the EU15 (Planistat Europe, Bradley Dunbar Associates, 2003a). The Commission is therefore well aware, by virtue of its long experience in developing policies for many island territories over many years, of the complex nature of the challenges facing island economies. Moreover, from an EU perspective, the evidence would suggest that insularity is indeed a clear ‘handicap’, or at least a much clearer ‘handicap’ than is the case among small states or across the global economy. For example, some 93% of EU islanders live within regions with a GDP per capita below that of the EU average (Eurisles Website, 2006)4. In addition, since the Commission has a clear objective of
2 The vast majority of EU islands are located within Western Europe – EU15. Apart from Estonia and, of course, Malta and Cyprus, there are very few offshore islands associated with the New Member States (NMS10 or NMS12). 3 Defined as (a) having an area of at least one sq. km. or 10 ha., (b) being at least 1 km. from the continent, (c) having a permanent resident population of at least 50 people, (d) having no permanent link (e.g. bridge or tunnel) with the continent, and (e) not housing an EU national capital. 4 This statistic is, arguably, a misleading one for two reasons. Firstly, the Eurostat data on which it is based frequently groups offshore islands in with adjacent (littoral) areas of the continent and it is impossible to identify separate GDP per capita values for the individual islands. Secondly, and perhaps much more importantly, islands 14 creating a much more highly integrated EU economy, insularity inevitably places barriers to integration and is therefore a characteristic ‘problem’.
The Planisat Europe report contains within it quite a clear exposition of how the complex nature of the islandness ‘handicap’ is currently viewed within the EU policy making process. Figure 1 sets out the conceptual framework model which was used as a basis for the research conducted for the Planistat report. The model has a number of key features:
• ‘Island status’, as can be seen in Figure 1, is one of a number of exogenous factors over which the local economy has no control. Interestingly, these exogenous factors are all geographical characteristics, the others being ‘outlying status/remoteness’ (from the EU market), small size and ‘natural conditions’ (e.g. being mountainous, climate). The model does not explicitly state that the different exogenous factors have exactly the same effects on the within-island system (‘endogenous factors in Figure 1), but clearly implies that insularity is one of a group of characteristics with similar impacts.
• A clear implication of the model, and one which has carried through into subsequent Cohesion policy debates and regulations, is that any one island may experience an accumulation of more than one geographical ‘handicap’ (e.g. by being both an island and mountainous); the greater the accumulation the stronger the policy intervention justified. In fact, this is simply a restatement of a principle which has long existed in EU policymaking. Good examples of this have been the Outermost Regions of the EU5 and the sparsely populated regions of northern Europe. The ORs are regions which combine extreme remoteness from the continental EU with small size and (for six of the seven entities) insularity. They have been accorded special policy measures within the EU for many years (see below). The sparsely populated regions of northern Europe combine the ‘handicaps’ of small (population) size with geographical remoteness and a harsh (for agriculture) climate. The accession of Sweden and Finland in 1995 brought a large swathe of sparsely populated regions into the EU for the first time and led to these regions being accorded their own
are highly concentrated within only a few member states (e.g. only five member states account for over 75% of all islands, and 95% of the population in the islands is concentrated on the big Mediterranean islands of Corica, Sicilia, Sardegna, Baleares and Crete – Planistat Europe, Bradley Dunbar, 2003a). Hence the low average GDP per capita may be largely a reflection of the within-EU regional location of the big island populations (i.e. the Mediterranean region) rather than inherent islandness ‘handicaps’. In a recent paper, Armstrong and Read (2005) analyse data for the 35 EU15 islands for which useable Eurostat data exist and find that when islands’ GDP per capita values are compared with contiguous EU regions (rather than the overall EU15 GDP per capita), there is no evidence that islands are systematically disadvantaged. On the contrary, as with the global small island states, it is found that islands are to be found among both the most prosperous and least prosperous categories or regions within the EU. 5 These comprise the French Départements d’Outre Mer of Gualeoupe, Reunion, Martinique and French Guyana, together with the Açores, Madeira and Islas Canarias of Portugal and Spain. Of these, only one (French Guyana) is not an island. 15 special status (a new ‘Objective 6’ of the Priority Objectives of the Structural Funds in the 1994-99 Structural Funds programming period).
• Figure 1 shows clearly that the complexity of the relationships between insularity and the inner workings of island systems is well understood. In particular, the transportation element (shown as the ‘Access to markets, transport problems’ box in Figure 1) is clearly identified as being only one effect of insularity, and moreover is seen to be only one factor among many within a highly interrelated within-island system. Hence “from these (exogenous, geographical) constraints, a whole series of effects, with powerful interactions, affect the territory studied” (Planistat Europe, Bradley Dunbar, 2003a, p.8).
• Figure 1 shows that insularity can have powerful effects across many different dimensions of the life of an island community. Some of these are economic, such as the ‘Limited production possibilities’ box in Figure 1, picking up a major theme of the small states research literature; and the ‘Limited human resources’ and ‘Limited natural resources’ boxes, which also pick up key themes from the small states literature. Others, however, are more concerned with standards of living (the ‘Access to public services’ box), the environment (the ‘Environmental problems’ box) or demography, with many islands having an ageing populations as younger residents move off-island to find better job opportunities (the ‘Demography’ box in Figure 1).
• The Planistat Europe report drew upon the conceptual framework to undertake a principal components analysis of an array of variables designed to quantify the eight endogenous boxes set out in Figure 1. No attempt was made to analyse the causal relationships between the various variables. Nevertheless, the results obtained are interesting in that the first (most important) component (50% of the variance) was dominated by remoteness/isolation variables (of which six were incorporated in the study)6, followed by geomorphological conditions (e.g. climate, altitude etc, and including a measure of the size of the archipelago of which the island is a part – 38% of variance) and then size (only 8% of variance). These results are interesting in that they tend to support the results of much of the small states and international growth model research which frequently place remoteness as the most important of the geographical variables (particularly since ‘landlocked’ status is usually interpreted as also picking up relevant accessibility issues such as the difficulties such countries have in accessing major global maritime routes).
6 These were distance to be travelled to meet 15 times the population of the territory, distances island/continent and island/capital of the mother country, number of means of transport, differences between GDP and that of the surrounding population, tonnes of freight per capita and number of passengers transported per capita). 16 The EU view of the effect of insularity can therefore be seen to be a suitably subtle one, recognising the complexity of the relationships involved. However, it is clear from the language used (e.g. island status as a ‘handicap’, ‘limited’ production possibilities etc) that insularity is not seen as having many advantages. In this respect, the EU view is now somewhat at variance with the emerging consensus within the small states literature and with the evidence which has been produced by international economic growth models. In these other two literatures insularity is seen as having advantages as well as disadvantages and other geographical characteristics as seen as being stronger handicaps (e.g. landlocked status being more significant as a handicap than either smallness or insularity).
The Commission’s view of insularity as a ‘handicap’ has carried over into major policy decisions for the 2007-13 Cohesion policy programmes in two main ways. Firstly, the Outermost Regions of the EU have been granted an extension of the special legal status they already enjoyed within the EU treaties (and which they were first granted in the Treaty of Amsterdam in 1997). This special status allows them to enjoy special policy treatment by way of many different national subsidies and policy concessions and through a series of EU treaty derogations (e.g. on ceilings for state aids under Article 87(3)(a) and (c) of the Treaty of Rome and on certain types of taxation). These confer major advantages in respect of agricultural, transport, fisheries and certain types of industrial policies (e.g. freeport zones in Madeira and the Canarias). Over the years, the various concessions and derogations have developed into a formidable collection of policy instruments and subsidies (European Commission, 2004). A significant on-going debate is whether the assistance being given should be focused most strongly on strengthening integration with the EU or with with other countries much closer to the ORs (an important issue for the ORs in the Caribbean, South America and Indian Ocean). This debate remains largely unresolved, with assistance in the 2007-13 period continuing to be allowed for both of these types of policies.
In addition, the Outermost Regions will continue to benefit from special treatment within EU Structural Funds and agriculture policy programmes in the 2007-13 period. The ORs have been given the privilege of Objective 1 status in successive Structural Funds programming periods since 1989 irrespective of whether they meet the GDP per capital eligibility criterion, and have also been able to access special Structural Funds Community Initiatives (Pseidon, Poseima, Poseican and Interreg). They have also long enjoyed special agricultiure policy subsidies for crops such as bananas, tobacco etc and a range of additional fisheries policy subsidies. This special status for the ORs is to continue into the 2007-2013 programming period.
17 Secondly, the Commission has recognised ‘islandness’ as one of a number of geographical handicaps deserving of special policy treatment in regions of the EU within the EU heartland on the continent of Europe. In addition to insularity, the 2007-13 Cohesion policy programmes will take special account of other geographical handicaps, namely mountainous terrain (Nordregio, 2004), very low population density regions, geographically remote regions, and isolated rural areas. Of these, only the sparsely populated regions had enjoyed special Structural Funds benefits (following the accession of Sweden and Finland in 1995). In the 2007-2013 Cohesion policy programmes these geographical handicaps will be systematically incorporated into the new Objective 2 programmes (European Commission, 2004).
3. Building Typologies for Island Economies: A Cluster Analysis
3.1 The Greek and GB case studies
As has been shown in section 2, island economies face particular social and economic challenges, but also may well have a number of important advantages too. In this section an attempt is made to identify differences within and between Greek and British islands in their relative economic performance and to speculate on possible reasons for the differences identified. The results presented are preliminary in nature and represent the first part of an ongoing project. In this paper the results are presented of a classification, drawing upon a cluster analysis, of the economies of a number of islands which are part of Greece and Great Britain.
Islands from the member states of Greece and Great Britain have been selected for the following reasons:
• They are two of those five EU member states which contain within them the vast majority of EU islands. The results should therefore be of interest not only in themselves, but also because they cover a large number of EU islands and in numbers which allow sufficient degrees of freedom for statistical analysis to be conducted for the islands of each country independently.
• Since the research is concerned with geographical characteristics (of which insularity is one), the Greek and GB offshore islands have a number of extremely interesting similarities and differences which make them good case studies for comparative research. They are at opposite geographical extremes of the EU, with most of the islands being very remote from the main EU markets (the Scottish islands, which dominate the GB data set face onto the Atlantic Ocean, and the Greek islands being mostly in the Aegean face onto a
18 non-EU country, Turkey, which at the time of the study data sets, 2001, had borders with Greek islands which were largely closed in nature). Moreover, many of the islands in the data set are also mountainous, parts of bigger archipelagos, have small populations, have widely differing climates, and have varying degrees of accessibility to their main national markets (all are rather remote from the main EU markets). In other words, there is wide variability in the other main geographical variables identified by the literature and by the European Commission as being important.
• Both Greece and Britain conducted comprehensive population censuses in 2001, and their census questionnaires contain many similar questions, a feature which facilitates comparisons7. Data from the population censuses form the bulk of the data sets utilised.
• Concentrating on the analysis of islands data from national statistical sources allows many more islands from the two countries to be incorporated than would have been the case had a full set of EU15 or EU25 countries been selected. Eurostat data is highly deficient for the analysis of island economies (see Armstrong and Read, 2005; Planistat Europe, Bradley Dunbar, 2003 for discussions of the limitations of Eurostat data for islands research).
In the context of the research described in this paper, the island economies of Britain and Greece have been classified on the basis of an array of geographical, economic and demographic variables. The resulting classification has then been used to address the following questions:
• Do certain British and Greek islands share similar economic performance?
• Are there distinctive features of certain groups of Greek and British islands?
• Can British and Greek islands be grouped according to their size and remoteness? As noted earlier, cluster analysis is used to build a typology for the two sets of island economies.
3.2 The data sets
Islandness
An initial decision which all researchers interested in studying island economies must make is how to define an ‘island’. This is more complex than it appears at first sight, even if one concentrates solely on inhabited islands only. As has already been noted, the 2003 Planistat
7 We have not, however, sought to combine the two sets of islands into a single data base for analysis because there remain significant variations in the manner in which data are available from the two censuses). 19 study for the European Commission identified 286 EU15 islands using the following definition based on five objective criteria. An island must:
• have an area of at least one square kilometre or 100 hectares;
• be at least one kilometre from the continent;
• have a permanent resident population of at least 50 people;
• have no permanent link with the continent;
• not house an EU capital (Planistat Europe, Bradley Dunbar Associates, 2003). This definition goes beyond the sole common point shared by islands, that they are surrounded by water. It imposes a lower limit as regards size, requires a minimum human presence and eliminates islands too near the coast or connected by a fixed link to the continent. In our opinion, this definition is too strict and is not wholly logical. There is clear logic in excluding uninhabited islands (on the grounds of zero interest as far as economic performance is concerned). There is logic in also excluding very small population islands (on the grounds of extreme data problems which can be avoided by their exclusion with the majority of island populations and GDP still being retained within the data sets). There is also clear logic in excluding islands which house an EU capital, since capital cities have major economic functions which the vast majority of islands cannot aspire to (i.e. they are extremely unusual ‘outliers’ in any data set). Excluding islands containing capitals has the further advantage that this excludes the giant ‘outlier’ case of Great Britain itself. On the other hand, we can see little logic in imposing a minimum of 1 km. distance from the continent, since the nature of transport costs is such that any separation, however short, will trigger the important transhipment elements. On the other hand, there is logic in excluding islands with fixed (bridge or tunnel) links to the continent. This logic is, however, not as clear as it may appear at first sight. Firstly, many of the fixed links (especially the all important bridge links rather than the rarer tunnel links) are subject to tolls. They therefore remain a barrier to integration. Moreover, most bridges are subject to regular closure as a result of weather conditions, and in this respect more closely resemble maritime routes than they do ordinary road links.
In this study we have therefore used a definition of an ‘island’ which draws upon only three of the five Planistat criteria – land area of at least 1 sq. km, population of at least 50 and no EU capital. Hence the database includes islands that are connected to the mainland as well as inshore coastal islands. Based on these three criteria, some 60 British offshore islands and 63 Greek islands were incorporated into the data bases. Tables 1 and 2 provide lists of the selected islands, together with the NUTS 3 regions of which they are a part.
20 Data sources
After compiling databases of islands, the next step was to obtain data for the various indicator variables for the islands that would allow us to perform a cluster analysis. Research on global small island states usually faces severe data problems because of limited availability of indicators. This situation is also evident in the pan-EU context, since the lowest geographical level at which appropriate data are available is usually at NUTS 3 level. Many islands are smaller than the requirements for NUTS 3 classification and often islands are aggregated together (into archipelagos) to create an appropriate NUTS 3 unit. Alternatively, and more common as well as more serious in its effects, is the tendency for many NUTS3 regions to aggregate offshore islands with a littoral area of the continent, making separate identification of the island’s statistics impossible. As noted earlier, this was one of the reasons why we have chosen not to work with pan-EU data sets.
Even when one works with rich data sets such as the population censuses, there are many problems encountered in constructing comparable and accurate data for small islands. Despite these potential limitations in data collection, it was possible to obtain data on a series of key variables from the national population censuses of Britain (i.e. England & Wales and Scottish censuses) and Greece as well as other secondary sources (including some Eurostat data). The key data sources used in this paper are the 2001 population censuses for England & Wales and Scotland, and the 2001 population census for Greece. Other indicators on accessibility and peripherality are obtained from Copus (1999).
Data on the British Islands
The censuses of population have been and still remain the most authoritative social accounting of people, housing, social conditions and (some) economic variables in Britain and remain unique sources of data for the social sciences (Rees, Martin and Williamson, 2002). The censuses record demographic and socio-economic information at a single point in time and are normally carried out every ten years. The most recent census in Britain was on 29 April, 2001. Data output is available at six different spatial levels from the 2001 census – government office regions, unitary authorities, counties, districts, wards and output areas. Output areas were designed specifically for statistical purposes on the basis of 2001 Census data and are built from postcode units. They are the lowest geographical level at which data may be retrieved from the UK census (Rees, Martin and Williamson, 2002).
21 Most of the islands in the British data set are Scottish islands (54 of the 60). Data on these Scottish islands were obtained from the General Register Office for Scotland. Data on the Welsh island of Anglesey as well as the English offshore islands were obtained from the England & Wales census. There were in fact 96 inhabited individual islands in Scotland in 2001. However, only 54 island groups are represented in the British data set. Even using census data, with all its richness, there remain problems in that the very smallest output units can still comprise groups of islands rather than individual islands. In the case of the Scottish islands there were a number of islands which did not meet the size criteria for an output area. In order to prevent the disclosure of information pertaining to individuals, output areas cannot contain less than 20 households. Islands not meeting this size criterion have been aggregated with other islands and mainland to create an ‘island group’. An island group may contain an individual island or a main island and other islands, which are so small that they have been merged in order to form an output area (Fleming, 2003). In this paper, the description ‘island’ indicates an individual island or island.
Of the 54 Scottish islands, 19 are island groups comprising more than one island8. Tresco in England is the only other island group in the complete data set of British islands.
Two of the British islands (Isle of Anglesey and Isle of Wight) have a Eurostat NUTS 3 region classification. The remaining 58 islands form part of a NUTS 3 region.
The full list of GB islands used is given in Table 1.
Data on Greek Islands
As it was the case with the British census, the Greek national census is collected every ten years and provides information on people, housing, social conditions and the economy in Greece. The last census was collected on Sunday 18 March 2001 and was the principal data source used for the Greek islands. Data may be retrieved from the national census of Greece at four spatial levels – regions, prefectures, municipalities and communities. Greece has 13 administrative regions, four of which (Ionian Islands, Crete, North Aegean and South Aegean) are comprised entirely of islands. Of 55 prefectures, 10 are individual islands or island groups. At the upper spatial levels, many larger islands are grouped with smaller adjacent islands to form a prefecture or municipality. Hence, data on the very smallest islands in the Greek data set is attainable only at the municipality or community spatial levels. In such cases, island
8 The Scottish island groups include Arran, Benbecula, Bute, Colonsay, Housay, Lewis and Harris, Luing, Mainland of Orkney, Mainland of Shetland, Mull, North Uist, Raasay, Rousay, Skye, Stronsay, Tiree, Tresco, West Burra and Westray. 22 groups are disaggregated at the next lower level. For example, the island Chios is a prefecture in the North Aegean region, whilst the islands Psara and Inousses are municipalities within the prefecture of Chios. Consequently, for the purposes of our analysis the data for Chios are disaggregated to the municipality level to obtain individual data for the islands of Psara and Inousses. All of the Greek islands, with the exception of Crete, have either a NUTS 3 region classification or form part of a NUTS 3 region. Crete alone is classified as a NUTS 2 region. Copus’ (1999) peripherality indices are at the NUTS 3 regional level. Hence, the appropriate index for Crete was obtained by averaging the indices for its three NUTS 3 regions (Lasithi, Rethymno and Chania). A list of the Greek islands is presented in Table 2
Variables The performance of cluster analysis on the British and Greek islands, using geographical and economic variables, was dependent upon the availability of statistical data. As mentioned in the previous section, the 2001 population censuses of England, Scotland and Wales as well as the 2001 Greek national census were the main sources for data on selected indicator variables. Although, comprehensive in their coverage of demographic, social and economic factors, certain relevant statistics, such gross domestic product levels and institutional indicators such as turnout for local elections were not available from the national censuses. In spite of these data constraints, several geographical and economic variables were obtained and included in the cluster analysis. Precise definitions of these variables are provided in the Appendix.
Geographical Characteristics of the Islands As discussed in the theoretical overview, islands are typically regarded as vulnerable because their special geographical features which may handicap their development. Insularity and mountainousness are two of the more common geographical characteristics identified with islands. These geographical characteristics, among others, pose special problems for islands as it relates to accessibility to and remoteness from major markets. The extent of the handicap posed differs among islands. Six geographical variables were included in the cluster analysis to account for the special characteristics of these island economies:
23 • Land area
• Population
• Population density
• Distance to the main capital
• Distance to Brussels
• Copus (1999) peripherality indices for the NUTS 3 region of which the island are apart.
• Presence of an airfield Geographical variables also provide information on the natural resource capacity of islands (e.g. land area) and their ability to facilitate the emergence of certain sectors, particularly an agricultural sector. Population related variables are also good indicators of the size of the domestic market and the labour force. These six geographical indicators allow us to include data on remoteness, accessibility to major markets and domestic markets capacity in our cluster analysis of British and Greek islands.
Economic Characteristics of the Islands
Several economic variables were included as measurements of economic performance in the selected British and Greek islands. Of particular importance, were the economic activity of these islands in terms of population activity rates, employment and unemployment rates. Islands with lower unemployment rates are regarded as more successful than their counterparts with higher levels of unemployment. Additionally, indicators of sectoral specialisation, in the islands, provide information on the proportion of the population engaged in agriculture, manufacturing and services. All economic variables used in the cluster analysis were obtained from the 2001 population census data for England, Scotland and Wales and Greece. The economic variables include:
• Activity rates
• Measures of employment
• Unemployment rates
• Sectoral breakdown
• Occupany levels
24 Although the selected indicator variables provide relevant and important measures of the geographical and economic characteristics of these islands, they are far from comprehensive. Mountainousness is an important geographical characteristic of islands, which influence their development, however data limitations precluded the inclusion of an appropriate mountainous indicator in the cluster analysis. Further, more comprehensive indicators on economic activity such gross domestic product levels were unavailable at the island level. This was mainly because the selected British and Greek islands are not sovereign economies.
3.3 Cluster analysis
Cluster analysis is a convenient method for summarising and retrieving information from a large set of data (Everitt, 1993). It is a classification technique widely used in both the natural and social sciences. The key function of this multivariate method is to group cases of data, by their characteristics, into clusters such that the objects in a cluster are relatively more homogenous (Bacher, 1996; Backhaus et al., 1996).
In performing cluster analysis, three principal choices must initially be made. These are the choice between case or variable clustering, the choice of cluster method and the choice of proximity coefficient as the basis of the cluster method (Everitt, 1993). Since the objective of this study is to build typologies of Greek and British island economies, case-by-case cluster analysis is the appropriate one to use rather than variable-by-variable analysis to distinguish different groups of islands. The cases are the islands and the variables are the economic and geographic characteristics that are used to group these islands into homogenous clusters.
There are two broad categories of clustering techniques - hierarchical methods and optimisation methods. Optimisation techniques, such as k-means, use a one-step clustering algorithm based on an optimisation function. Each case is assigned to its final cluster in a manner which ensures maximum final distances between clusters and minimum distances between cases within each cluster. In the k-means algorithm the number of final clusters must be specified in advance, a major limitation of the technique.
Hierarchical cluster analysis is by far the most popular approach and is invariably the default method in the main software packages (e.g. SPSS, used here). The classification algorithm consists of a series of groupings which run from (a) a single cluster containing all the cases to n clusters each containing a single case, or (b) n clusters each containing a single case to a single cluster containing all of the cases (the more popular agglomerative methods - Everitt, 1993). There are quite a large number of hierarchical cluster procedures. It is the widely used Ward’s
25 method which has been adopted in this analysis. Ward’s method has the advantage of generally giving a clear definition of clusters compared with other methods since ‘it will generally find tight minimum variance spherical clusters’ (Wishart, 1987, p.91). Ward’s algorithm joins cases into clusters such that at each step in the process every possible pair of cluster is considered, and the pair whose combination involves the smallest ‘information loss’ is combined (Everitt, 1993). Information loss is defined in terms of an error sum of squares criterion. The Ward statistic is expressed by the following equation:
2 k =g j=m i=nk W x x = ()ijk - jk ÂÂÂk =111j= i=
where x jk is the mean value of variable j in the cluster k , xijk is the value of an observation i assigned to cluster k , nk is the number of observation in cluster k, m is the number of variables, and g is the number of clusters. Using Ward’s agglomerative method, every island begins as an individual cluster in the algorithm. They are then joined together into groups in a step-by-step manner. Agglomeration continues until only a single cluster remains. The step-by- step process is usually represented as a dendogram (see Figures 2 and 3). There is no hard and fast rule on how many clusters one selects for examination since in principle the researcher can work with either one cluster, n clusters, or any number in between. In practice, researchers seek to identify the number of clusters to examine by locating a point to cut though the dendogram where a large numbers of cases are suddenly brought together by the algorithm (shown on the dendogram by a long vertical line above a cluster – see Figures 2 and 3 for the selections made in this study).
There are many different proximity coefficients and identifying the similarity or dissimilarity coefficient to be used to distinguish between the groups is an important step in performing cluster analysis. In this study, as is always the case with Ward’s method, squared Euclidean distance is the dissimilarity coefficient utilised. The Euclidean distance between two objects is expressed in terms of the following equation:
p d x x 2 ij = ()ij - ij Âk =1
26 The Euclidean distance between cases i and j is obtained by taking their scores on a variable, k , and calculating the distance. The smaller the Euclidean distance between two cases the more similar the cases are.
The results of the cluster analysis using Ward’s method as the grouping algorithm and squared Euclidean distance as the dissimilarity coefficient are set out as the dendograms in Figure 2 and 3. The following section discusses the results of applying the Ward’s method on Greek and UK islands data.
4. Typologies of UK and Greek Islands: Results of the Cluster Analysis
Two separate cluster analyses were conducted. The first was conducted on a data set comprising 18 variables and 60 island cases for the British small islands. The second was conducted on a data set comprising the same 18 variables, but this time for 63 Greek island cases. The 18 variables are listed in the Appendix. As can be seen, they are sub-divided into two groups. The first comprise geographical characteristics, focusing in particular on measures of size (land area, population, population density) together with alternative measures of accessibility (accessibility to the nation’s capital city – London and Athens, accessibility to the EU centre - Brussels, whether or not the island has an airport, and an index of accessibility to the whole EU). The second group comprises various measures of economic structure and performance (economically active population, male and female activity rates, working age population, employment rate, unemployment rates, self-employment, proportions of employment in agriculture, manufacturing and services and average rates of property occupation during the year).
The results of each of the two cluster analyses will be presented in turn, before differences and similarities between the two sets of results are explored. In each case a Ward’s method/squared Euclidean distance cluster analysis was conducted.
27 The British Offshore Islands
Examination of the dendogram for the cluster analysis of the 60 British offshore islands (see Figure 2) suggests that a four-cluster solution is the optimum one (in the sense of maximising within-cluster homogeneity and between-cluster differences). The four clusters identified are shown as separately numbered on Figure 2.
In order to understand how the cluster algorithm has produced the four clusters, and in order to clearly identify their different characteristics, the mean values for each of the 18 variables are calculated for each cluster in turn. These are set out in the first four columns of Table 3. The final column of Table 3 presents the overall mean value for each of the 18 variables, this time calculated across all 60 islands taken as a whole. Cluster mean values which are greater than the overall mean are picked out in Table 3 in bold numbers, with a shaded cell background. Table 8 presents the same set of results for the optimum six clusters identified by a cluster analysis of the 63 Greek islands, and can be interpreted in the same manner as Table 3. It is by scrutinising the values in Tables 3 and 8 that it is possible to build island typologies.
The mean values set out in Table 3 can be used to facilitate the labelling of the clusters. For instance, Cluster 1 of the British islands we have labelled as ‘Larger but lagging, dependent on tourism’. As can be seen from the cluster 1 column of mean values, this cluster of 23 islands exhibits generally rather poor economic performance with lower than average male activity rates (although female activity rates are relatively high), higher than average unemployment rates, lower than average employment rates, small working populations and low occupancy rates. These islands are generally larger ones on average (in land area terms), but with relatively low population densities. They exhibit roughly average accessibility values with respect to both London and the EU (note how close the cluster 1 mean values are to the overall mean values in the final column). They are not therefore by any means the most remote of the islands, a finding reinforced by the fact that a disproportionate number also have their own airport. Finally, as can be seen from Table 3, these islands are disproportionately dependent upon the service sector, which in island economy terms almost invariably means tourism. Agriculture and manufacturing are much less well represented. The presence of tourism also probably accounts for the higher than average female activity rates, the sole economic performance indicator in which the islands of cluster 1 do well, although even here the cluster value (44.30%) is only slightly higher than the overall average (43.93%).
28 Table 3: Mean values, by variable for the four British island clusters
Cluster 3:
Cluster 1: Cluster 2: Remote,
Larger but Small, but Cluster 4: lagging, remote and diversified Accessible, Overall tourism agriculture and successful and mean Variables dependent dependent successful diversified values 1. Geographical characteristics Land area 325.85 45.20 150.73 130.97 186.75
Population 2529.26 260.37 3083.58 23704.33 5211.35
Population density 18.35 9.68 24.66 205.09 45.31 Access to London 747.52 829.81 932.54 446.47 761.31 Access to EU 984.72 1013.64 1077.95 728.31 972.62
Perindex-ECU 85.86 90.78 95.33 74.23 87.32 Airfield 0.35 0.44 0.33 0.22 0.35 2. Economic performance Active population 45.54 52.64 52.04 59.21 50.78 Active male 55.70 58.12 57.33 51.67 56.07 Active female 44.30 41.87 42.67 48.33 43.93 Working age pop. 60.91 65.56 64.25 68.11 63.90 Employed 91.83 92.94 97.42 96.22 93.90 Unemployed 8.09 7.00 2.42 3.22 5.93 Self employed 27.04 43.62 22.50 28.33 30.75 Agriculture 13.82 29.69 11.83 6.17 16.51 Manufacturing 7.69 5.44 13.75 8.67 8.45 Service 56.13 43.94 61.00 66.67 55.43 Occupancy 75.39 74.62 90.00 72.67 77.70 N 23 16 12 9 60
Table 4 provides a list of the 23 islands that comprise cluster 1. As can be seen, the islands are all Scottish offshore islands, containing within them some of the larger (in terms of land area), but low population density west coast offshore islands (e.g. Arran, Bute, Jura, Mull, Skye, Harris and Lewis). These are popular tourist destination islands, but lack diversified sectoral structures. This group of Scottish islands forms the least economically successfully performing cluster of islands within the whole British data set. They have some agriculture and fishing, but
29 their mainstay is tourism from which they make a modest living. As shall be shown later, the nearest Greek equivalent islands to cluster 1 make a much better living from tourism than do the cluster 1 British islands. This is almost certainly a reflection of two factors: (a) the Greek islands command much larger absolute flows than the Scottish west coast islands (not as a result of greater remoteness, but almost certainly because of climate), and (b) many of the Greek islands can benefit from more types of tourism flows as a result of the much greater closeness of Athens to the Greek islands, whilst the Scottish islands are very distant from London and the other big British cities (enabling the Greek islands to access more day trip, overnight and weekend tourism than the Scottish islands).
Table 4: Larger but lagging, dependent on tourism.
Cluster 1: Larger but lagging, dependent on tourism
North Uist Seil
South Uist Arran
Unst Bute
Barra Jura
Benbecula Islay
Fetlar Mull
Great Bernera Luing
Vatersay Raasay
Scalpay Tiree
Eriskay Great Cumbrae
Skye Lismore
Lewis and Harris
Table 5 lists the 16 British islands that comprise cluster 2, which can be labelled as ‘Small, remote and agriculture dependent’. Returning to the mean values in Table 3, it can be seen that in the islands belonging to this cluster the agricultural sector accounts, on average, for no less than 29.69% of all economic activity (compared with the overall average for all islands of 16.51%, itself a high value by British regional standards). In addition, the average land area of these islands is a mere 45.20 square kilometres, despite which they still have below-average
30 size populations and population densities. The islands in this cluster exhibit all the characteristics of traditional agricultural economies – high male activity rates and levels of self-employment, but low female activity rates and a generally weak economic performance in terms of high unemployment and low employment rates. Property occupation rates are also low. Finally, as Table 3 again shows, these small, agricultural islands are relatively remote, both from London and also the wider EU, despite many of them nowadays having small airfields.
Taken together, clusters 1 and 2 comprise no fewer than 39 of the 60 British offshore islands. As shall be shown later, the Greek islands exhibit far smaller groups of poorly economically performing islands than is the case in Britain. In this respect the two sets of islands are very different from one another.
Table 5: Small, remote and agriculture dependent.
Cluster 2: Small, remote and agriculture dependent
Stronsay Flotta
Westray Berneray
North Ronaldsay Rousay
Papa Westray Shapinsay
Eday Housay
Gigha
Sanday
Coll
Eigg
Hoy
South Ronaldsay
Turning to the list of 16 islands in cluster 2, once again we see that the cluster comprises wholly Scottish islands, this time being extremely small and remote ones, many actually offshore from other (larger) Scottish islands. This time, unlike cluster 1, they include small islands from Orkney (e.g. Papa Westray, Hoy) as well as the west coast Scottish islands (e.g. Eigg, Coll).
Turning now to cluster 3, Table 3 shows that this comprises 12 islands. We have characterised this as ‘remote, but diversified and successful’. They are relatively successful since they are
31 characterised by relatively high employment rates (97.42%), working age population (64.25%), high male activity rates (57.33%), high self-employment (22.50%), and low unemployment (a mere 2.42%). These islands have diversified economies, with disproportionate shares of both manufacturing (13.75%) and services (61.00%), and a still-robust agriculture sector too (11.83%). These are, however, relatively remote islands, both from London and the wider EU, with low rates of airfield provision.
The combination of remoteness with both diversification and a relatively successful economy is an unusual one. Table 6, which lists all the islands that belong to this cluster, gives us the reason for this unusual cluster.
Table 6: Cluster 3: Remote, diversified and successful
Cluster 3: Remote, but diversified and successful
Mainland of Orkney
Mainland of Shetland
Whalsay
Fair Isle
Bressay
East Burra
Muckle Roe
Grimsay (North)
Yell
West Burra
Burray
Trondra
This cluster is dominated by Orkney (main island) and Shetland (main island). These are islands with extremely distinctive landscapes and culture which have been successful in attracting tourism, are big enough to have generated some manufacturing and, most importantly of all, in the case of Shetland have been able to enjoy high levels of income from oil companies exploiting rich offshore oil reserves. Shetland in particular has used the oil rents
32 to diversify its economy. This is the sole case for the islands of both countries where (apart from fish stocks) there is a significant natural resource endowment.
Finally, Table 3 identifies one further significant cluster, cluster 4. We have characterised it as ‘accessible, successful and diversified’. It is interesting that this cluster contains only nine islands. It shows once again that the British offshore islands are mostly less successful economic entities, with the two successful clusters (i.e. clusters 3 and 4) containing relatively few of the 60 islands of the full data set.
Table 7: Accessible, diversified and successful islands of England and Wales
Cluster 4: Large, accessible, diversified and successful
Colonsay
Iona
St. Agnes
St. Martins
Tresco
St. Mary's
Anglesey
Isle of Wight
Isle of Walney
The average population of these islands is 23,704.33, which is nearly five times the overall average for all UK islands. However, scrutiny of Table 7 which lists the nine islands in cluster 4 shows that the mean value in this case is dominated by Anglesey (in Wales) and the Isle of Wight (in England). The remaining seven are much smaller islands, as is reflected in the land area value of only 130.97 sq. km. More important is the fact that these are generally successful and diversified economies. In particular, economic activity rates are relatively high (59.21% when the average for all GB islands is only 50.78%), high employment rates, high female activity rates and low unemployment rates (3.22%). The manufacturing and service sectors are disproportionately large (8.67% and 66.67% respectively), and there remains a substantial agricultural sector (6.17%). Finally, these are the least remote of all the British islands, both in terms of access to London and to the wider EU. The cause of the low remoteness mean values is apparent when one scrutinises Table 7 – this cluster is dominated by the main islands of England (Isle of Wight and the Scilly Isles) and Anglesey (north Wales), all of which are more
33 accessible than their Scottish counterparts. There are only two Scottish members of this contingent of islands.
In summary:
• The 60 British offshore isles have been divided by the cluster analysis into four groups, two of which are relatively economically unsuccessful (clusters 1 and 2) and two of which are relatively successful (clusters 3 and 4).
• The numbers of islands which fall within the relatively unsuccessful clusters are more numerous than those in the successful clusters (39 of the 60 islands are in clusters 1 and 2).
• It is the more diversified economies which are the successful islands. Those which are highly specialised on either just tourism (cluster 1) or agriculture & fishing (cluster 2) tend in a British context to be less successful.
• There does seem to be a systematic adverse effect of remoteness – accessibility matters. Of the two less successful clusters, cluster 2 are remote islands, whilst cluster 1 is made up of islands which are close to the mean value for remoteness, which is very high for the British islands given the predominance of Scottish islands in the set (very distant from both the UK national capital, London and also the rest of the EU). The most successful cluster (cluster 4) is made up almost wholly of the most accessible islands in England and Wales. The sole exception to the rule that ‘accessibility matters’ is cluster 3. As we have seen, however, this is dominated by Shetland (and to a lesser extent Orkney), where access to income flows from oil resource rents have had an important effect. A good natural resource endowment (and ability to access some of the oil rents even though the local governments are relatively weak in the UK9). Cluster 3 is therefore the ‘exception that proves the rule’ as far as remoteness is concerned.
There does not seem to be any systematic relationship between island size (either land area or population) and economically successful performance.
9 Shetland is a classic example of how a local government can negotiate favourable deals with oil companies seeking to build terminals within a region, even where the natural resource is not a locally owned resource. 34 The Greek islands
Applying the same cluster analysis methodology to the 63 case Greek islands data set resulted in the identification of six distinct clusters (see Figure 3). Table 8 sets out the cluster mean values for the same 18 variables that were used to build the Greek island typologies.
Table 8: Mean values, by variable for the six Greek island clusters
Cluster 4: Cluster 2: Ionian Cluster 5: Cluster 1: Small, islands, Inshore, Small, remote from Cluster 3: large, diversified, remote from Athens and Accessible, dependent but mixed EU and the EU ad successful on agric. economic agriculture agriculture and And performanc Cluster 6: Overall dependent dependent diversified tourism e Crete mean values Variables 1. Geographical characteristics Land area 80.90 69.42 419.86 276.69 443.98 8336.00 372.58 Population 1293.87 2507.71 23227.07 26585.87 28250.70 601131. 23797.57 00 Population density 17.33 38.50 64.27 96.37 114.60 72.00 59.56
Access to Athens 169.43 296.41 210.69 332.54 80.75 316.97 216.45 Access to EU 2223.22 2347.82 2251.13 1809.06 2063.98 2381.73 2182.21 Perindex-ECU 95.09 98.75 97.05 92.93 89.30 97.84 95.22 Airfield 0.13 0.21 0.80 0.37 0.10 1.00 0.35
2. Economic performance Active population 38.33 36.21 39.07 37.75 37.20 44.00 37.87 Active male 70.47 73.71 66.53 67.50 71.30 62.00 69.87 Active female 29.53 26.29 33.47 32.50 28.70 38.00 30.13 Working age 62.47 66.79 67.13 65.25 68.10 67.00 65.86 Employed 94.20 83.14 90.27 86.87 88.00 89.00 88.81 Unemployed 5.80 16.86 9.73 13.12 12.00 11.00 11.19 Self employed 28.53 19.43 15.33 22.12 15.70 23.00 20.43 Agriculture 25.40 16.57 8.93 17.12 8.40 22.00 15.71 Manufacturing 3.73 3.86 5.33 4.12 6.60 6.00 4.68 Service 39.27 47.21 56.47 49.25 53.40 51.00 48.82 Occupancy 35.93 46.00 49.60 56.25 45.90 65.00 46.05 N 15 14 15 8 10 1 63
35 Once again, as in Table 3, values with greater than average mean scores for a given variable are picked out in bold type and with a shaded cell background.
The first distinctive feature of Table 8 is cluster 6. This contains a single island case, Crete. In some ways this is reassuring. Crete is by far the biggest island both in terms of land area (land area 8,336 squared kilometres, when the overall average for all Greek islands is 372.58 squared kilometres) and population (over 600,000 compared to an average of only 24,000 persons) of all of the islands within both the Greek and British data sets, as the land area and population values for the cluster 6 column in Table 8 show. Crete is a big, not very accessible island (to either Athens or the EU), but with excellent airport connections for tourists from mainland Greece and northern Europe. As a result, it is relatively successful, with one of the highest employment rates, a large service sector and very frequent fast ferry-boat connections to the Athens port of Pireaus. It can hardly be described as inaccessible or isolated. It is truly an outlier case in statistical terms and it is identified as such by the cluster analysis. It is, moreover, both a relatively remote island, but also a diversified and relatively successful one. There does therefore, in this case, seem to be a size effect. Extremely large islands, such as Crete, may be able to overcome the disadvantages of remoteness.
However, we must be careful not to jump too quickly to this conclusion – we have a sample of only one such large island in our data sets, and Crete has outstanding natural environment and climate advantages which have made it a major tourist destination for northern EU citizens. This may be a resource effect at work here and not a size effect. Our analysis is incapable of resolving this issue.
Leaving Crete to one side, and hence concentrating on the other five clusters, cluster 1 is a large group comprising 15 islands. We have labelled this described as a ‘Small, remote from EU and agriculture dependent’ cluster. The closest equivalent to this cluster in Britain is cluster 2 (made up largely of Scottish small islands). In the cluster 1 Greek islands, agriculture accounts for 25.40% of all economic activity (the average value for all Greek islands is 15.71%). As with their Scottish equivalents, the cluster 1 Greek islands also show the characteristic features of agricultural economies – high male activity rates but low female activity rates, high rates of self employment and a high overall activity rate. The only difference between cluster 1 in Greece and its equivalent in Britain (cluster 2) is that the Greek islands have a relatively low unemployment rate whereas this was higher than average in the British cluster 2. Occupancy rates are again, however, low. These are not very economically successful islands. Moreover, as with their British counterparts, these are generally remoter islands and lack good airport facilities. However, as Table 8 shows, this group are remoter than
36 other Greek islands from the EU, but not from the national capital, Athens. This shows that it is possible for agriculture dependent islands to continue to exist relatively close to Athens, if they are small enough and (presumably) if they lack good marine ferry and air links to Athens. Table 9 lists all the islands that make up the Greek cluster 1.
Table 9: Small, remote from EU and agriculture dependent Greek islands
Cluster 1: Small, remote from EU and agriculure dependent
Serifos
Sifnos
Antiparos
Kea
Kythnos
Agios Efstratios
Folegandros
Schinoussa
Amorgos
Kimolos
Sikinos
Kythera
Skyros
Anafi
Irakleia
About two thirds of these islands are located in the administrative prefecture of Cyclades, located south east of mainland Greece. It is noteworthy that this is a relatively prosperous island region of Greece with very frequent ferry-boat services. Nevertheless, the Cluster 1 comprises most of the smallest inhabited islands of the Cyclades region, For instance, Antiparos is a very small island (1037 enumerated inhabitants in the 2001 census and land area 34.83 squared kilometres) located very near the much larger island of Paros. Antiparos does not have frequent direct ferry services to Pireaus and the other large islands of the region, but is
37 indirectly connected to them via Paros (which is a very well developed island and, according to our analysis, it belongs to Cluster 3 discussed below). Likewise, Kimolos is similar island (769 inhabitants and a land area of 35.71 squared kilometres) located near the larger island of Milos. It is noteworthy that most of the islands in Cluster 1 belong to the so called unprofitable shipping line which is known in Greece as the “losing line” (in Greek “ μμ” which literally means the “unproductive line” or the “infertile line”). Boats serving the islands on this line are typically subsidised by the Greek state. As noted above, most of the islands of this cluster belong to the Cyclades archipelago. The rest of the islands belong to administrative prefectures elsewhere in the Aegean Sea. For instance, Agios Efstratios (371 inhabitants, 43.23 squared kilometres) is a very small island which is located in the North Aegean and belongs to the prefecture of Lesvos (and is very near the large and relatively prosperous island of Lesvos which belongs to Cluster 3 discussed below). Also, Skyros belongs to the Sporades archipelago (also comprising the more prosperous and accessible islands of Skiathos, Skopelos and Alonissos, that belong to Cluster 5 discussed below).
Returning to Table 8, it can be seen that Cluster 2 is also a large group, comprising 14 islands. These too, like cluster 1 have a disproportionately large agriculture sector (16.57%) and are again on average small islands (the average cluster mean for land area is 69.42 sq. km. against the overall average of 372.58 sq. km.). These islands are even more similar than cluster 1 to their British equivalents (cluster 2 in Britain). In this case they have relatively high unemployment rates (on average 16.86% when the overall average for all Greek islands is 11.19%), and they have many of the characteristic features of rural economies (i.e. high male, but low female activity rates), although self-employment rates and workforce sizes are slightly below the overall Greek islands average. As with the British cluster 2 islands, these are not successful economies. They are, moreover, as with their British counterparts relatively remote, this time both from Athens and the wider EU (the distance from Athens is on average 296.51 km against an overall Greek island average of 216.45 km.). This cluster has therefore been labelled as ‘Small, remote from both Athens and EU and agriculture dependent’. Table 10 lists all the islands that belong to Cluster 2. It is noteworthy that half of these islands are located in the Dodecanese region, located in the southeast Aegean Sea and off the South West coast of Turkey. All but one (the island of Ios, which belongs to the Cyclades archipelago) of the rest of the islands in this cluster are located in the East and north-East Aegean. It can be argued that the islands this Cluster of islands is very similar to the equivalent of the Scottish North Atlantic cluster (Cluster 2 of the British islands) as the islands in both clusters are very similar in terms of size, accessibility and economic development. The Scottish North Atlantic islands face onto the Atlantic Ocean, whereas the Greek Cluster 2 islands are far from the Greek mainland and
38 face onto Turkey, which is a non-EU country and has very limited trade and other links with the Greek islands. It is noteworthy that when compared to Cluster 1 of Greek islands discussed above, Cluster 2 islands are similar in terms of size and dependence on the agricultural sector, but they are much more remote than their Cluster 1 counter-parts. It is also worth noting that as it was the case with islands such as Antiparos in Cluster 1, there are some very small islands in Cluster 2 which are very near larger islands that are relatively more prosperous. For instance, Psara (located in the northern Aegean) is a very small island (422 inhabitants, 39.77 squared kilometres) which is located very near the much larger and more prosperous island of Chios (belonging to Cluster 3 discussed below). Similarly, most of the Dodecanese islands in this cluster are located near the much larger and prosperous islands of Rhodes and Kos.
Table 10: Small, remote from both Athens and the EU, and agriculture-dependent Greek islands
Cluster 2: Small, remote from both Athens and EU, and agriculture dependent
Kalymnos
Symi
Patmos
Inousses
Nisyros
Ios
Tilos
Psara
Thirasia
Karpathos
Kasos
Astipalaia
Leipsoi
Agathonisi
39 Taking the Greek clusters 1 and 2 together (as agriculture dependent, relatively remote and small), and comparing them with their (single cluster) British counterparts (cluster 2), it can be seen that they share many common features. The principal differences are solely:
• Not all of this type of island in Greece is highly remote (at least in terms of how the crow flies). Cluster 1 islands are remote from the EU, but not from Athens. This highlights a key difference between the Greek islands and those of Britain. The principal metropolitan centre of the nation (and source of most national tourists, second home owners and the like) is much closer to the main sets of islands in Greece than it is in Britain. The Athens ‘economic shadow’ therefore is likely to fall over many more of the Greek islands than does London (and the other big British cities) does for the British offshore islands, most of which are far away in northern Scotland. Despite this difference, it is clear that relatively unsuccessful agriculture-based small island economies can continue to exist even close to Athens. Whether this is the result of poor local marine and air links is an issue which is beyond the scope for this paper and must await further research.
• Britain has rather more of this type of traditional rural small island economy (39 of 60 islands in the set – well over half) than is the case in Greece (29 of the 63 islands in the set – under half).
Turning to the Greek cluster 3, Table 8 shows this is another large group, made up of 15 islands. We have labelled this as ‘Accessible, successful and diversified’ and it is a cluster with a close British counterpart (cluster 4 on Table 3). Like the British cluster 4, the Greek cluster 3 is a relatively successful set of islands (low unemployment, high activity rates – especially for females, a high employment rate, high residential occupancy rates, and a high workforce rate). The cluster is also diversified, with both manufacturing and services (especially tourism one suspects) over-represented. The British equivalent cluster was made up of English and Welsh islands which were highly accessible to both London and the wider EU. The situation for Greece’s cluster 3 is slightly different – the islands are relatively accessible to Athens, but remoter than average from the EU. However, scrutiny of Table 8 shows that cluster 3 islands have good airport provision (the best of all the Greek island clusters) and one suspects that excellent airport access to northern Europe is actually greatly reducing the remoteness from the EU that islands in this cluster exhibit.
Table 11, which lists the islands in cluster 3 shows that many of the islands in this cluster are located in the East Aegean Sea and away from the Greek mainland. For instance, Rhodes and Kos are located 428.70km and 323.92km away from Athens respectively, but they both have airports (as well as frequent ferry-boat connections to the port of Pireaus, which however is not
40 captured by this data set) and are more connected to the Greek mainland compared to the islands in cluster 2.
Finally, as with the British counterpart cluster (cluster 4), the Greek cluster 3 is not dominated by just large islands; there are small islands in this group too.
Table 11: Accessible, successful and diversified Greek islands
Cluster 3: Accessible, successful and diversified
Kos
Rhodes
Milos
Andros
Tinos
Paros
Mykonos
Leros
Syros
Thira
Chios
Samos
Naxos
Lesvos
Donoussa
The remaining two Greek island clusters (excluding Crete – cluster 6) have no directly comparable British counterparts. Taking cluster 4 in Greece first, Table 8 shows that this is a small cluster comprising only eight islands. This is a very interesting cluster in terms of its characteristics. It is the most accessible of all Greek island groups to the main northern EU markets, and it has better than average airport infrastructures. On the other hand, it is relatively remote from Athens. Economic performance is a mixed one. The service sector is well developed, with presumably strong tourism sectors, but there is also still a disproportionately 41 large dependence on agriculture – manufacturing is poorly developed. These are more like dual economy islands than diversified ones. The result is a mixed economic performance picture – good rates of self employment and high female activity rates (as is characteristic of tourist economies), but also relatively high unemployment, low employment rates and low male activity rates. These are far from being uniformly successful island economies. Finally, cluster 4 is made up of relatively large (average population of 26,586 persons) population islands.
Table 12 solves the mystery of this unusual Greek island cluster. It is dominated by the Ionian Sea islands located off the western seaboard of Greece. These are islands with good agricultural conditions, closer to the rest of the EU than all other Greek islands (an advantage enhanced by good airport infrastructure, and hence strong tourism flows), and they are usually large islands. It is their distance from Athens which is their main disadvantage, hence the unusual combination of agriculture and tourism, and hence the mixed picture of economic performance. We have labelled these ‘Ionian islands, large, dependent on both tourism and agriculture’. There is no equivalent British island cluster counterpart.
Table 12: Ionian islands, large, dependent on tourism and agriculture.
Cluster 4: Ionian islands, large, dependent on both tourism and agriculture
Othoni
Paxi
Ithaki
Lefkada
Kefalonia
Zakynthos
Kerkyras
Erikoussa
Finally, for Greece there is cluster 5. This too has no direct British counterpart. As Table 8 shows, cluster 5 comprises 10 islands which, as table 13 shows, are all located in the Aegean Sea. We have labelled this cluster ‘Inshore, diversified, mixed economic performance’. As Table 8 shows, most of these islands are very accessible. Indeed, they are by far the most accessible of all of the Greek islands to Athens (and indeed, the Greek mainland in general (and hence the national market) since this group includes both Evia and the Sporades group of
42 islands comprising Skopelos, Alonissos and Skiathos). They also have better than average accessibility to the rest of the EU. For instance, Aegina is located only 39 km away from Athens, a distance which can be covered in about 30 minutes on a high speed boat.
Apart from Skiathos, these islands are not major tourist destinations for visitors from northern Europe and beyond. On the other hand, these are islands which are popular locations for second homes for residents of Athens and the other mainland Greek cities. Moreover, they are accessible enough to attract weekend and overnight visitors, and in some cases (e.g. Salamis, Aegina) day trippers too from Athens.
Table 13: Inshore, diversified, mixed economic performance islands islands
Cluster 5: Inshore, diversified, mixed economic performance
Agistri
Skopelos
Alonissos
Aegina
Spetses
Hydra
Poros
Skiathos
Evia
Salamina
Cluster 5 is an extremely interesting cluster in that it reveals that accessibility need not always be the benefit that the research literature implies. In the case of the cluster 5 islands, one can perhaps argue that they are too close for comfort to Athens and the other mainland big cities. Large levels of second home ownership in a community can severely damage social cohesion and economic performance. The influx of visitors at a weekend or at even more infrequent intervals leaves the communities with very little in the way of income streams for long periods during the week and across many months of the year. Moreover, whilst weekend visitors do pump money into the local economy by way of hotel rentals and the like, day trippers spend relatively little money locally, and can often bring a lot of the food, drink and other goods they need with them.
43 In other words, the very proximity to Athens and the other mainland Greek cities may be having a profound and unusual effect on the types of tourism and visitors. There is no British counterpart to this cluster since virtually all of the British islands are so far from London and the other large English cities that this sort of impact is extremely small. The result of the types of visitors to the inshore Greek islands is to generate a decidedly mixed economic performance. On the one hand, male activity rates and workforce rates are quite high, but there are none of the other benefits enjoyed by other Greek tourist island – female activity rates are low, self- employment is low since it is hard to sustain small businesses with so much second home ownership, overall employment rates are low and unemployment rates are high. This would appear to be one situation where good accessibility is operating to the detriment of the islands’ economic performance.
5. Conclusion
This paper has drawn upon cluster analysis to examine the relationship between various measures of economic performance, and a number of geographical characteristics (particularly size, population density and various measures of accessibility to national and EU markets). The paper compares two very different sets of island economies – 60 offshore British islands and 63 Greek islands. The data principally refer to the year 2001, when population censuses were conducted in both countries.
The main findings of the paper are as follows:
• The analysis has revealed that clear and distinctive clusters of islands can be identified (across the 18 variables used within the cluster analyses) within both Greece and Britain. Moreover, it has been possible to interpret a coherent picture of the reasons for these very different clusters. Moreover, the different clusters show quite wide variations in the levels of economic performance.
• Land area and population size does not seem to be systematically related to economic performance. In this respect, the findings of the paper are in accord with previous research. The sole possible exception to this finding is the island of Crete, which is just so large (and successful), compared to the other islands in the data set, that the cluster analysis separated it out as a separate cluster in its own right.
• Whilst the British and Greek islands do share a number of common characteristics (the reason why we selected the two countries for the analysis in the first case), there turned out also to be some important differences. Hence the two sets of islands are relatively remote
44 from the main EU markets. Moreover, the north western Scottish island seem to share with Greece’s eastern Aegean (Dodecanese) islands a particularly severe degree of remoteness from both national and EU markets and being at the very edge of the EU (with the North Atlantic lying beyond the Scottish islands and Turkey – whose border was largely closed to the Greek islands in 2001 – lying beyond the Dodecanese islands). A final similarity is that many of the islands in both countries have developed large tourism-related service sectors. On the other hand, there turned out to be important differences. Greek islands have a major climate advantage for summer tourists who are willing to travel long differences to enjoy this resource. The offshore British islands lack this climatic advantage and must rely on different types of visitors. There is also a major difference in the accessibility characteristics of the two sets of islands. The British islands are virtually all both remote from the EU and also their national capital city, whereas although Greek islands are also remote from the wider EU, many of them are extremely close to both Attiki and other Greek mainland population centres. Finally, the Shetland Isles of Britain are the sole group of islands with a significant natural resource endowment – oil. This too turned out to be an important feature influencing the results obtained.
• Given the similarities and differences between the two sets of islands, it is perhaps not surprising that in some cases the clusters identified are almost identical in their characteristics whilst others are not:
Clusters 1 and 2 from the Greek analysis are very similar indeed to cluster 2 from the British analysis. These tend to be small, relatively remote islands which have retained a higher than average dependency on agriculture and which, as a result, perform relatively poorly. They are mainly north western Atlantic islands in Britain and Dodecanese islands in Greece. Interestingly however, in Greece there is also a set of these small islands which are remote from the EU but quite close to Athens (cluster 1) and yet do not seem to have (yet) experienced any major benefits (or costs) from their proximity to Athens.
Cluster 4 from the British analysis and cluster 3 from the Greek analysis comprise islands which have benefited from their good accessibility characteristics to develop successful and diversified economies. In the British case these are mainly English and Welsh offshore islands rather than Scottish.
Here the similarities end, and there is one British cluster and three Greek clusters which have no equivalents in the other country. In Britain, cluster 4 is made up of remote, but diversified and successful islands, This cluster is dominated by the Shetland Islands
45 with their access to (parts of) the offshore oil resource. In Greece, the analysis picks out three unusual clusters: (a) Crete – an outlier case which perhaps ought to have been left out of the analysis, (b) cluster 4, the Ionian Sea islands which are large, relatively agricultural productive and more accessible than other Greek islands to northern EU tourist markets, as a result of which they are relatively successful, and (c) a very interesting cluster of 10 Greek islands (cluster 5) which are inshore islands very accessible to Athens. This is the one case where accessibility seems to have worked to the disadvantage of the island economies since this group of small inshore islands have economies distorted by the presence of many holiday homes, day trip and weekend tourists, which would appear to be less lucrative types of tourist markets than found elsewhere among the Greek islands.
This paper remains very much work in progress. It is apparent from the results obtained that the various accessibility measures we have used remain inadequate. In particular, we will seek in future research to refine the maritime and air transport accessibility measures so that they more adequately reflect the reality on the ground. For example, our ‘Airfield’ measure is a simply ‘dummy’ variable which cannot differentiate size of airport or destinations served (or number of airports – for instance Crete has two international airports). The economic performance measures too need to be extended, and data on GDP, incomes, migration and human capital endowments would be very helpful. Finally, the research would benefit from the inclusion of detailed resource (especially fish, oil) and climate variables, since these appear to have been important in separating out some of the clusters. A variable which measures how mountainous each island is would also be worth incorporating given the importance of mountain regions in European Commission thinking on geographical ‘handicaps’.
46 Appendix: Description of Variables Used in the Cluster Analysis
As has been noted in the text of the paper, the variables used in the cluster analysis can be classified into two broad types – geographical characteristics and economic performance characteristics. The full list of the 18 variables used, and their precise definitions are set out in the table below.
Variables Definition Geographic Characteristics British Census Greek Census Land area Total surface area in square Total surface area in square kilometres kilometres Population Total number of permanent Total number of permanent residents residents Population density Residents per square Residents per square kilometre kilometre Access to national capital city Great circle distance in Great circle distance in kilometres to London kilometres to Athens Access to European Union Great circle distance in Great circle distance in kilometres to Brussels kilometres to Brussels Peripherality Index-ECU Copus (1999) peripherality Copus (1999) peripherality index for the 1998 NUTS 3 index for the NUTS 3 region of which the island region of which the island is a part using GDP as the is a part using GDP as the mass variable in current mass variable in current prices (Euros) prices (Euros) Airfield Presence or absence of an Presence or absence of an airfield airfield
Economic Characteristics Active population Total economically active Total financially active population (age 16 -74) as population (age 10 and a percentage of total above) as a percentage of population total population Active male Total economically active Total financially active male (age 16 -74) as male (age 10 and above) as percentage of total active percentage of total active population population Active female Total economically active Total financially active female (age 16 -74) as female (age 10 and above) percentage of total active as percentage of total population active population Working age Population aged 15-64 as Population aged 15-64 as percentage of total percentage of total population population
47
Variables Definition Economic Characteristics British Census Greek Census Employed All people aged 16-74 in All people aged 10 and employment as a above in employment as a percentage of total active percentage of total active population population Self employed All people aged 16-74 self- All people aged 10 and employed as a percentage above self- employed as a of total employed percentage of total employed Unemployed All people aged 16-74 All people aged 10 and unemployed as a above unemployed as a percentage of (total percentage of (total employed and total employed and total unemployed) unemployed) Agriculture All people aged 16 – 74 in All people aged 10 and employment working in above employed in agriculture, hunting, agriculture, animal forestry and fishing as a breeding, forestry and percentage of total active fishing as a percentage of population total active population Manufacturing All people aged 16 – 74 in All people aged 10 and employment working in above employed in manufacturing as a manufacturing as a percentage of total active percentage of total active population population Services All people aged 16 - 74 in All people aged 10 and employment working in above employed in services services (retail and (retail and distribution, distribution, hotel and hotel and restaurants, restaurants, transport, transport, storage and storage and communication, financial communication, financial intermediation and real intermediation and real estate, public estate, public administration and defence, administration and defence, education and health and education and health and social work) as a social work) as a percentage of total active percentage of total active population population Occupancy Total occupied dwellings Total occupied dwellings as a percentage of total as a percentage of total dwellings dwellings
48 References
Ahlfeld, S., Hemmer, H-R., and Lorenz, A. (2005), The Economic Growth Debate – Geography versus Institutions: Is There Anything Really New?, Discussion Paper in Development Economics No. 34, Justus-Liebig-Universität, Giessen.
Armstrong, H.W., and Read, R. (2000), ‘Comparing the Economic Performance of Dependent Territories and Sovereign Micro-States’. Economic Development and Cultural Change, Vol. 48, pp. 285-306.
Armstrong, H.W. and Read, R. (2002), ‘The Importance of Being Unimportant: The Political Economy of Trade and Growth in Small States’. In Murshed, S.M. (ed.), Issues in Positive Political Economy (London: Routledge), pp. 71-88.
Armstrong, H.W. and Read, R. (2003a), ‘The Determinants of Economic Growth in Small States’, The Round Table, Vol. XCII, pp. 99-124.
Armstrong, H.W. and Read, R. (2003b), ‘Small States, Islands and Small States That Are Also Islands’. Studies in Regional Science, Vol. 33, pp. 1-24.
Armstrong, H.W. and Read, R. (2005), ‘The Structural Funds and the Outermost Regions of the European Union’, Paper submitted to Journal of Common Market Studies.
Armstrong, H.W., Johnes, G., Johnes, J., and MacBean, A.I. (1993), ‘The Role of Transport Costs as a Determinant of Price Level Variations Between the Isle of Man and the United Kingdom’. World Development, Vol. 21, pp. 311-318.
Armstrong, H.W., de Kervenoael, R.J., Li, X., and Read, R. (1998), ‘A Comparison of the Economic Performance of Different Micro-States and Between Micro-States and Larger Countries’. World Development, Vol. 26, pp. 639-656.
Atkins, J.P., Mazzi, S., and Easter, C.D. (2000), A Commonwealth Vulnerability Index for Developing Countries: The Position of Small States, Commonwealth Economic Paper Number 40 (London: Commonwealth Institute).
Bacher, J. (1996), Clusteranalyse, Munchen:Oldenburg
Backhaus, K., Erichson, B., Plinke, W. and Weiber, R. (1996), Multivariate Analysemethoden, Springer, Berlin
Baldacchino, G. (2006), 'Managing the Hinterland Beyond: Two Ideal-Type Strategies of Economic Development for Small Island Territories', Asia Pacific Viewpoint, Vol. 47, pp. 45-60.
49 Bertram, G. (2003), ‘On the Convergence of Small Island Economies with their Metropolitan Patrons’, World Development. Vol. 32, pp. 343-364.
Bertram, G. (2006), 'Introduction: The MIRAB Model in the Twenty-First Century', Asia Pacific Viewpoint, Vol. 47, pp. 1-14.
Bertram, G. and Karagedikli, XX (2004), ‘Are Pacific Economies Converging or Diverging?’. In Poot, J. (ed), On the Edge of the Global Economy (Cheltenham: Edward Elgar, forthcoming).
Bertram, G. and Watters, R.F. (1985), ‘The MIRAB Economy in South Pacific Microstates’, Pacific Viewpoint, Vol.26, pp. 497-519.
Bhaduri, A., Mukherji, A., and Sengupta, R. (1982), ‘Problems of Long Term Growth in Small Economies: A Theoretical Analysis’. In Jalan, B. (ed.), Problems and Policies in Small Economies (London: Croom Helm), pp. 49-68.
Briguglio, L. and Galea, W. (2003), Updating the Economic Vulnerability Index, Islands and Small States Institute, Occasional Paper Number 2003/4 (Valetta: University of Malta).
Chai, S-K. (1998), ‘Endogenous Ideology Formation and Economic Policy in Former Colonies’. Economic Development and Cultural Change. Vol. 46, pp. 263-290.
Cobb, S.C. (2001), ‘Globalization in a Small Island Context: Creating and Marketing Competitive Advantage for Offshore Financial Centres’, Geografiska Annaler, Vol. 83B, pp. 161-174.
Copus (1999), A New Peripherality Index for the NUTS III Regions of the European Union. Report prepared for the European Commission. Regional Policy and Cohesion.
European Commission (2002), Report from the Commission on the Implementation of Article 299(2) of the EC Treaty: Measures to Assist the Outermost Regions, COM (2002) 723 final (Brussels).
European Commission (2004), A New Partnership for Cohesion, Convergence, Competitiveness and Cooperation: Third Report on Economic and Social Cohesion, Office for Official Publications of the European Communities, Luxembourg.
Everitt, B.S. (1993), Cluster Analysis, 3rd ed. (London: Edward Arnold).
Fleming, A.D. (2003), ‘Scotland’s Census 2001 Statistics for Inhabited Islands’. Occasional Paper, No. 10, General Registrar Office for Scotland.
50 Hampton, M. and Abbott, J.S. (1999), Offshore Finance Centres and Tax Havens: The Rise of Global Capitalism (Ichor Business Books: Indiana).
Kuznets, S. (1960), ‘The Economic Growth of Small States’. In Robinson, E.A.G. (ed.), The Economic Consequences of the Size of Nations (London: Macmillan), pp. 14-32.
Milner, C., and Westaway, A. (1993), ‘Country Size and the Medium Term Growth Process: Some Country Size Evidence’. World Development, Vol. 21, pp. 203-212.
Nordregio (2004), Mountain Areas of Europe: Analysis of EU Member States, Acceding and Other European Countries, Final Report, DG Regional Policy (Brussels: European Commission).
Planistat Europe, Bradley Dunbar Associates (2003a), Analysis of the Island Regions and Outermost Regions of the European Union: Part I: The Island Regions and Territories, Report No. 2000.CE.0.AT.118 to the European Commission, DG Regional Policy, Brussels.
Planistat Europe, Bradley Dunbar Associates (2003b), Analysis of the Island Regions and Outermost Regions of the European Union: Part II: The Outermost Regions, Report No. 2000.CE.0.AT.118 to the European Commission, DG Regional Policy, Brussels.
Putnam, R.D., with Leonardi, R., and Nanetti, R.Y. (1993), Making Democracy Work: Civic Traditions in Modern Italy (Princeton NJ: Princeton University Press).
Read, R. (2002), ‘Growth, Economic Development and Structural Transition in Small Vulnerable States’. In Murshed, S.M. (ed.), Globalisation, Marginalisation and Development (London: Routledge), pp. 171-184.
Rees, P., Martin, D. and Williamson, P. (2002), ‘Census data resources in the United Kingdom’, in P. Rees, D. Martin and P. Williamson (eds) The Census Data System. Chichester: Wiley, pp. 1–24
Sachs, J. (2003), Institutions Don’t Rule: Direct Effects of Geography on Per Capita Income, National Bureau of Economic Research, Working Paper 9490, Cambridge MA.
Sachs, J. and Warner, A. (1997), ‘Sources of Slow Growth in African Economies’. Journal of African Economics. Vol. 6, pp. 335-376.
Wishart, D. (1987), Clustan User Manual. 4th ed. (St. Andrews, Scotland: University of St. Andrews).
51 Table 1: List of British Islands
Island NUTS 3 Region
Anglesey Isle of Anglesey
Arran Lochaber, Skye and Localsh and Argyll and the Islands
Barra Eilean Siar/Western Isles
Benbecula Eilean Siar/Western Isles
Berneray Eilean Siar/Western Isles
Bressay Shetland Islands
Burray Orkney Islands
Bute Lochaber, Skye and Localsh and Argyll and the Islands
Coll Lochaber, Skye and Localsh and Argyll and the Islands
Colonsay Lochaber, Skye and Localsh and Argyll and the Islands
East Burra Shetland Islands
Eday Orkney Islands
Eigg Lochaber, Skye and Localsh and Argyll and the Islands
Eriskay Eilean Siar/Western Isles
Fair Isle Shetland Islands
Fetlar Shetland Islands
Flotta Orkney Islands
Gigha Lochaber, Skye and Localsh and Argyll and the Islands
Great Bernera Eilean Siar/Western Isles
Great Cumbrae Lochaber, Skye and Localsh and Argyll and the Islands
Grimsay Eilean Siar/Western Isles
Housay Shetland Islands
Hoy Orkney Islands
Iona Lochaber, Skye and Localsh and Argyll and the Islands
Islay Lochaber, Skye and Localsh and Argyll and the Islands
Isle of Walney West Cumbria
Isle of Wight Isle of Wight
Jura Lochaber, Skye and Localsh and Argyll and the Islands
Lewis and Harris Eilean Siar/Western Isles
Lismore Lochaber, Skye and Localsh and Argyll and the Islands
52 Luing Lochaber, Skye and Localsh and Argyll and the Islands
Mainland of Orkney Orkney Islands
Mainland of Shetland Shetland Islands
Muckle Roe Shetland Islands
Mull Lochaber, Skye and Localsh and Argyll and the Islands
North Ronaldsay Orkney Islands
North Uist Eilean Siar/Western Isles
Papa Westray Orkney Islands
Raasay Lochaber, Skye and Localsh and Argyll and the Islands
Rousay Orkney Islands
Sanday Orkney Islands
Scalpay Eilean Siar/Western Isles
Seil Lochaber, Skye and Localsh and Argyll and the Islands
Shapinsay Orkney Islands
Skye Lochaber, Skye and Localsh and Argyll and the Islands
South Ronaldsay Orkney Islands
South Uist Eilean Siar/Western Isles
Stronsay Orkney Islands
St. Agnes Cornwall and Isles of Scilly
St. Martin's Cornwall and Isles of Scilly
St. Mary's Cornwall and Isles of Scilly
Tiree Lochaber, Skye and Localsh and Argyll and the Islands
Tresco Cornwall and Isles of Scilly
Trondra Shetland Islands
Unst Shetland Islands
Vatersay Eilean Siar/Western Isles
West Burra Shetland Islands
Westray Orkney Islands
Whalsay Shetland Islands
Yell Shetland Islands
53 Table 2: List of Greek Islands
Island NUTS 3 Region
Aigina Attiki
Agathonisi Dodekanisos
Agios Efstratios Lesvos
Agistri Attiki
Alonissos Magnisia
Amorgos Kyklades
Anafi Kyklades
Andros Kyklades
Antiparos Kyklades
Astipalaia Dodekanisos
Chios Chios
Crete Crete
Donoussa Kyklades
Erikoussa Kerkyra
Evia Evvoia
Folegandros Kyklades
Ydras Attiki
Inousses Chios
Ios Kyklades
Irakleia Kyklades
Ithaki Kefallinia
Kalymnos Dodekanisos
Karpathos Dodekanisos
Kasos Dodekanisos
Keas Kyklades
Kefalonia Kefallinia
Kerkyras Kerkyra
Kimolos Kyklades
Kos Dodekanisos
Kythira Attiki
54 Kythnos Kyklades
Lefkada Lefkada
Leipsoi Dodekanisos
Leros Dodekanisos
Lesvos Lesvos
Milos Kyklades
Mykonos Kyklades
Naxos Kyklades
Nisyros Dodekanisos
Othoni Kerkyra
Paros Kyklades
Patmos Dodekanisos
Paxi Kerkyra
Poros Attiki
Psara Chios
Rhodes Dodekanisos
Salimina Attiki
Samos Samos
Schinoussa Kyklades
Serifos Kyklades
Sifnos Kyklades
Sikinos Kyklades
Skiathos Magnisia
Skopelos Magnisia
Skyros Evvoia
Spetses Attiki
Symi Dodekanisos
Syros Kyklades
Thira Kyklades
Thirasia Kyklades
Tilos Dodekanisos
Tinos Kyklades
Zakynthos Zakynthos
55
Figure 1: Planistat Europe Conceptual Framework
Source: Planistat Europe, Bradley Dunbar Associates (2003a), p.9.
56 Figure 2: Cluster Analysis of UK Islands Using Ward’s Method
Case-by-Case Analysis
0 5 10 15 20 25 +------+------+------+------+------+-
North Uist (2) 37 South Uist 47 Unst 55 Barra 3 Benbecula (3) 4 Fetlar 16 Great Bernera 19 Vatersay 56 Scalpay (Harris) 42 Eriskay 14 Arran (3) 2 Bute (2) 8 Seil 43 Islay 25 Mull (4) 35 Jura 28 Luing (4) 31 Raasay (2) 39 Tiree (2) 52 Great Cumbrae 20 Lismore 30 Lewis and Harris 29 Skye (6) 45 Stronsay (3) 51 Westray (2) 58 North Ronaldsay 36 Papa Westray 38 Eday 12 Gigha 18 Sanday (Orkney) 41 Coll 9 Eigg (6) 13 Hoy 23 South Ronaldsay 46 Flotta 17 Berneray (North U 5 Rousay (3) 40 Shapinsay 44 Housay (2) 22 Mainland of Orkne 32 Mainland of Shetl 33 Fair Isle 15 Whalsay 59 Bressay 6 East Burra 11 Muckle Roe 34 Grimsay (North) 21 Yell 60 West Burra (2) 57 Burray 7 Trondra 54 St. Agnes 48 St. Martin's 49 Tresco (2) 53 Colonsay (2) 10 St. Mary's 50 Iona 24 Anglesey (Welsh) 1 Isle of Wight 27 Isle of Walney 26 Figure 3: Cluster Analysis of Greek Islands Using Ward’s Method Case-by-Case Analysis
0 5 10 15 20 25 +------+------+------+------+------+
Serifos 50 Sifnos 51 Antiparos 9 Keas 25 Kythnos 31 Agios Efstrat 3 Folegandros 16 Schinoussa 49 Amorgos 6 Kimolos 28 Sikinos 52 Kythera 30 Skyros 55 Anafi 7 Irakleia 20 Kalymnos 22 Symi 57 Patmos 42 Inousses 18 Nisyros 39 Ios 19 Tilos 61 Psara 45 Thirasia 60 Karpathos 23 Kasos 24 Astipalaia 10 Leipsoi 33 Agathonisi 2 Kos 29 Rhodes 46 Andros 8 Tinos 62 Milos 36 Paros 41 Mykonos 37 Syros 58 Leros 34 Thira 59 Chios 11 Samos 48 Naxos 38 Lesvos 35 Donoussa 13 Othoni 40 Paxi 43 Ithaki 21 Lefkada 32 Kefalonia 26 Zakynthos 63 Kerkyras 27 Erikoussa 14 Agistri/Agkistrio 4 Skopelos 54 Alonissos 5 Aegina 1 Spetses 56 Hydra/Ydras 17 Poros 44 Skiathos 53 Salamina 47 Evia 15 Crete/Kriti 12
1