Marine Policy 42 (2013) 245–252

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Marine Policy

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n CorrE-m

journal homepage: www.elsevier.com/locate/marpol

Identifying fisheries dependent communities in EU coastal areas

Fabrizio Natale a,n, Natacha Carvalho a, Michael Harrop b, Jordi Guillen c, Katia Frangoudes d

a European Commission, Joint Research Centre, Institute for the Protection and Security of the Citizen, Maritime Affairs Unit, Italyb European Commission, Eurostat, Regional Indicators and Geographical Information Unit, Luxemburgc IFREMER, UMR AMURE, Unité d'Economie Maritime, Franced Université de Brest, UMR AMURE, IUEM, France

a r t i c l e i n f o

Article history:Received 28 February 2013Received in revised form18 March 2013Accepted 18 March 2013

Keywords:Fisheries employmentCoastal communitiesAccessibilityGISGravity models

7X/$ - see front matter & 2013 Published by Ex.doi.org/10.1016/j.marpol.2013.03.018

esponding author. Tel.: +39 0332 789181.ail address: fabrizio.natale@jrc.ec.europa.eu (F

a b s t r a c t

The importance of local communities relying on fisheries is constantly emphasised in the EuropeanUnion's Common Fishery Policy. Previous studies have analysed fishery employment for the entire EUbased on statistical figures aggregated by administrative units at the regional or provincial level. Thispaper adopts a geographical approach to identify EU coastal communities relying on fisheries usingaccessibility analysis, principles at the basis of gravity models and disaggregated population andemployment statistics. The dependency on fisheries is calculated comparing estimated employmentfrom fisheries at each port with general employment in the areas of accessibility surrounding the port. Byconsidering spatially disaggregated statistics the importance of fishing activities for specific localcommunities emerges more clearly in respect of previous studies. The map of fisheries dependentcoastal communities identifies in 2010, 388 communities, out of 1697, with dependency ratios above 1%.Around 54% of total fishery employment is estimated in these areas. In terms of policy support,identifying and mapping these local fishing coastal communities is of key importance considering thestrong priority assigned by the new European Union's Common Fishery Policy to fishery management atthe regional level.

& 2013 Published by Elsevier Ltd.

1. Introduction

The fishing and aquaculture sectors often play a crucial role incoastal areas of the European Union (EU) and many coastalcommunities rely on these activities for their income, havinglimited possibilities for economic diversification. The EuropeanCommission in the new Common Fishery Policy is committed toactively promote growth and improve employment opportunitiesin coastal fisheries and aquaculture-dependent communities [1].This priority is in line with the more general target of the Europe2020 strategy to reach an employment ratio of 75% in the 20–64years old by 2020.

For a relatively small sector like fisheries, specific policy andfishery management measures in certain regions are requiredconsidering the social and economic importance that fishing fleets,especially small-scale, may play in these regions. This is ofparticular relevance in order to avoid potential negative impactsof policy measures on dependent communities.

The structural components of the CFP, which requires MemberStates to phase out overcapacity in their fleets by reducing thenumber of vessels, impact on fishery related employment.

lsevier Ltd.

. Natale).

Furthermore, increased capital investments in the sector toimprove efficiency and productivity often lead to the replacementof labour. While fish stocks are declining, high fuel prices, andincreasing competitive pressures from other sectors furtherexacerbate many coastal fishing communities inherent socio-economic vulnerability. The current EU policy in fisheries depen-dent areas, laid out in Axis 4 of the European Fisheries Fund (EFF,2007–2013) and in the future, more integrated, European Marineand Fisheries Fund (EMFF, 2014–2020), is to compensate for thesenegative effects and to provide support through investments in jobcreation and training programmes. For this, it is essential toidentify areas whose local economies are most dependent onfisheries so that these efforts to reduce the negative impacts canbe targeted effectively. Several studies have been aimed at mea-suring the contribution of fisheries to employment in coastal areasand identifying coastal communities relying on fisheries. A recentstudy estimated global marine fisheries employment at around260 million, or 203734 million full-time equivalent jobs [2].These estimates on fisheries generated employment (50 millionpeople engaged in the catching sector and 210 million in theprocessing sector) are 1.75 times greater than estimates by theFood and Agriculture Organisation (35 million employed in thedirect fisheries sector and 105 million in the indirect sector). Thestudy of Teh and Sumaila [2] presents a more comprehensiveestimate of global employment from all aspects of fisheries sectors,

F. Natale et al. / Marine Policy 42 (2013) 245–252246

including small scale operations that are not well monitored andgenerally located in smaller, rural communities where it is difficultto account for unlicensed fishers. Nearly half (22 million) of theestimated 50 million direct fisheries jobs are small scale, a 40%increase from previous FAO estimates.

In the EU, the relevance of fisheries employment and thedependency on the sector has been assessed by comparingemployment statistics on the fishery sector and on generalemployment at provincial and regional level [3–6].

According to Salz and Macfadyen [4], in 2005 the total employ-ment in the fisheries sector, including aquaculture, processing andcatching activities, amounted to about 407 thousand peoplerepresenting 0.2% of total EU employment. Considering only thecatching sector, employment was estimated at 187,200 jobs (0.09%of total EU employment).

Given the concentration of the fishing industry, the EdinburghEuropean Council of December 1992 officially recognised theexistence of Areas Dependent on Fishing and the need to givethem special attention. Based on 1997 data, around 34 areas with asector dependency rate of 3–15% were identified at the regionallevel and around 30 areas with a rate between 20 and 60% at thelocal administrative unit level [6].

In Macfadyen et al. [5] the level of dependency on fishery incoastal areas has been estimated comparing fishery employmentwith general employment statistics at regional level. The resultsindicate a dependency rate at above 0.1% in twenty regions, most ofthem concentrated in Greece (8) and French overseas territories (4).

A report by MRAG [7] considers 24 case study locations toexplore regional social and economic impacts of change infisheries-dependent communities. The study points out how datacollection across the diverse set of locations presented a number ofchallenges deriving from different levels of aggregation of data,different of boundaries, mismatches between the area of the casestudy location and administrative boundaries.

Fishery employment studies in the EU rely on economic data onthe fishing sector collected and assembled through the Data CollectionFramework (DCF) [8], while reference figures on general employmentstatistics are available from EUROSTAT at the regional level.

DCF fisheries economic data is collected by EU Member Statesthrough annual sampling programs. The data is then assembled forthe entire EU by the European Commission Joint Research Centre.This data is aggregated by country and fleet segment and currentlylacks detailed geographical segmentation by regional, provincial orlocal administrative units. In the case of the biological andtransversal data, also collected under the DCF, the reference tofishing areas provides the possibility to perform spatially explicitbio-economic modelling and to relate fishing effort to fish stocksin specific sea areas. On the contrary on the “land side” the higheraggregation of economic data by country hinders at the momentthe possibility of performing EU-wide socio-economic studies atregional or local level.

The need for spatially disaggregated statistics at a more finelydefined geographical scale is in general considered important forregional analyses [9]. This need is particularly relevant whenconsidering the socio-economic role of the relatively small EUfishing industry, which mostly affects local communities. Whentrying to evaluate a rate of dependency for the identification ofcoastal fishing communities this need applies not only to thespecific fisheries sector but also to the reference figures on generalemployment or GVA against which to compare.

The objective of this paper is to provide a method to overcomethe current limitations in data availability for regional socioeconomic studies for the fisheries sector with EU-wide coverage.In particular, focus is given to analysing fisheries employment at amore refined geographic scale and to identify and map coastalcommunities for which fishing activities are particularly relevant.

For this purpose several spatial analysis methods and indivi-dual vessel information from the Community Fleet Registerwere used.

First, fisheries employment national figures at each fishing portwere spatially disaggregated using a linear model between fleetcomposition and national employment figures.

Secondly, accessibility analysis was performed to define areasof influence for fishing ports (hereafter referred to as FisheriesService Areas or FSA). The idea stems from the approach of thegravity model, which considers the flow of goods and peoplebetween locations as being proportional to the relative offer anddemand at the place of origin and destination and inverselyproportional to distance. This model has been applied successfullyin many areas of economic geography such as international trade,human migration and in defining areas of influence for commer-cial centres [10]. Recent contributions are providing strongertheoretical foundations to the model and increasing acceptanceamong economists [11]. In regards to labour market analysis,gravity models reflect the tendency of people to gravitate towardsjob opportunities closer to their place of living. While job oppor-tunities generate an attracting force in respect of residential areas,high distance, low accessibility and long commuting time nega-tively influence the probability of a person being employed at agiven location.

Accessibility analysis using geographical focal points of attrac-tion has been applied to define areas of economic influence thatmirror human behaviour in a geographical context rather thansimply referring to aggregations by administrative units, such asprovinces or regions. Following this line, EUROSTAT defined socalled Maritime Service Areas considering areas of attractivenessand inland influence for focal points of attraction (ports andcoastal settlements) along the EU coastline [12]. This approach ishere extended considering fishing port as the main point ofinterest for job opportunities.

Thirdly, reference figures of general employment in the FSAwere obtained from a method that combines land cover maps withinformation from a soil sealing layer to produce so-called dasymetricpopulation densities maps [13]. The high resolution disaggregatedpopulation data provided by these maps is used as a proxy todisaggregate general employment from official statistics at theregional level.

The final step was to combine the estimated expected employ-ment generated by fishing activities at each port and the generalemployment in the boundaries of the FSAs. The ratio betweenthese two figures reflects the relevance of fishing activities interms of employment opportunities in the surrounding areas andis applied as indicator of the dependency rate of coastal commu-nities on fishing activities.

The result of the study is a map identifying fisheries dependentcommunities for the entire EU, by year and classes of vessels length.This result is important for targeting and assessing the impacts ofthe Common Fishery Policy that is calling for the adoption ofspecific measures for these communities. Further studies are fore-seen for spatially disaggregating other socio-economic data on thefisheries sector, such as labour productivity and to further analyse“land based” impacts of management policies at a local and regionalscale while maintaining a general EU coverage.

2. Methods

2.1. Disaggregation of fishery employment

Data on the EU fishing fleet and fishing ports were obtainedfrom the Community Fleet Register [14]. The fleet register includesinformation on the length, main fishing gear, engine power and

F. Natale et al. / Marine Policy 42 (2013) 245–252 247

port of registration for all commercial fishing vessels registered inthe EU. The register gives the full history of each vessel indicatingchanges in the registration and licensing status. By selecting activevessels with an operating licence at a given date it was possible toreconstruct the fleet composition by port over time.

The fleet register also provides fishing port coordinates; how-ever, since these coordinates are not always accurate a geo codingexercise was performed to complete and verify them on the basisof port, country and provinces names. The results of the automaticgeo coding services provided by Google Earth Pro and coordinatesfrom the World Port Index were compared with declared coordi-nates in the fleet register and checked for consistency with thedistance from the coastline to obtain the most reliable position forfishing ports. The fleet register offers a unique opportunity toperform regional socio-economic studies for the entire EU, as longas it can be proved that the port of registration corresponds to themain location on land where the vessel's economic activity can berelated and where employment opportunities are most likelygenerated. To validate this assumption, the area of activity of asample of vessels from one country during a year was analysed,using data on the origin and destination of fishing trips and thevolume and places of landings.

To estimate the employment generated by the fishing sector ateach port, a linear model was fitted using fleet composition andemployment figures at the national level between 2004 and 2010from the Scientific, Technical and Economic Committee for Fish-eries (STECF) annual economic report on the fishing fleet [15]. Themodel is based on a simplified relationship between employmentand three main vessel length classes, independently of time,country and specific vessel characteristics according to the follow-ing equation:

E¼ β1v1 þ β2v2 þ β3v3 ð1Þwhere E is the total national employment for catching activitiesbetween 2004 and 2010, and v1 v2 and v3 are the number ofvessels registered as active in the length classes: below 12 m,between 12 and 24 m and above 24 m, respectively.

The parameters for each vessel length class obtained from themodel (see Table 1 for the model summary statistics) were appliedto vessels in each port at the beginning of each year to calculatepotential employment generated by the fishery sector between2004 and 2010.

2.2. Delineation of areas of influence for fishing ports

Accessibility analysis was performed to delineate the areas ofinfluence of individual fishing ports. The inputs for the analysiswere a point dataset with the location of fishing ports and theTele-Atlas MultiNnet 2009 data set, which provides comprehen-sive road and ferry lines information for network analysis androuting applications. Fisheries Service Areas (FSA) were calculatedfrom these datasets using the ESRIs ArcInfo Network Analyst tool.The tool locates the nearest section of the road network to thefishing port as the starting point for the FSA calculation. It thengenerates, for each fishing port, a polygon covering all accessible

Table 1Model for the estimation of fishery employment.

Coefficient s.e.

β1 1.0875 (0.07498)β2 2.8084 (0.37234)β3 16.1393 (1.28849)R-squared 0.9684 –

F(3,122) 1248 –

p-value o2.2e-16 –

streets and ferry connections within the specified impedance of25 min from the port. A single commuting time of 25 min acrossthe EU was used according to the Fourth EU Survey of WorkingConditions, which indicates that workers spend on average 21 mineach way for travelling to and from work.

2.3. Calculation of reference employment data and comparisonwith fisheries employment

Data on employment at the regional level was obtained fromofficial statistics on active population and unemployment rates.Missing data in the time series were inputted by linear interpolation.

Regional employment was disaggregated spatially on the basisof population densities from the high resolution map for Europe[16]. The map covering the EU territory (except Greece) gives, inraster format for each cell of 100�100 m, the estimated popula-tion counts from original population statistics at the communelevel from the 2006 national census. The number of peoplegravitating in the area of accessibility of a fishing port wascalculated by summing the population values for each cell fallingin the corresponding FSA.

In cases of neighbouring ports with overlapping FSAs thepopulation counts in intersecting areas were divided in equalparts among the relative fishing areas allowing people to gravitateexclusively towards one of the overlapping fishing ports. Thiscorresponds to a simplified application of the gravity model inwhich equal probabilities are applied to determine the populationshare migrating towards competing focal points of attractionindependently of specific distance and size of the connectedpopulation centres and focal points.

The number of people employed was calculated from popula-tion values in proportion to the ratio between population andemployed persons at the regional level.

In the case of Greece and Cyprus, given the lack of coverage inthe EU population map the disaggregation of employment figuresat regional level was carried out considering a uniform populationdistribution based on the surface size of the FSA.

Finally, the ratio between potential jobs opportunities gener-ated by the fishing sector against the total employed populationgravitating to fishing ports was used to express the relevance offishing activities to local communities Fig. 1.

3. Results

While major fishing ports were directly geo coded throughreferences in the World Port Index, geo coding of minor fishing portsin the fleet register was not always possible due to spelling errors orlocal names missing from the reference lists of ports and places. Of the2051 ports in the fleet register, geo coding was considered acceptablein 1756 cases (86%). The study, although not applicable to all fishingports in the fleet register, covers 79% of the total number of vessels and83% of total gross tonnage of the EU fishing fleet in 2010.

The linkage of vessels to the port of registration in the fleetregister is a key assumption for allocating the fishery employmentto a specific geographical location. To validate this assumption, asample of 2153 vessels from the fleet register and 53,160 fishingtrips from logbook data were analysed (Fig. 2). The results showedthat 71% of vessels had between 80 and 100% of their trips leavingor departing from the port of registration. Around 74% of trips leftor arrived at the port of registration or at ports within 10 km fromthe port of registration. Around 75% in number, 57% in quantityand 63% in value of landings was at either at the port ofregistration or at ports within 10 km from the port of registration.These results prove that overall vessel activity patterns arerobustly linked to the port of registration. As expected, this link

= Dependency ratio

National fisheryemployment statistics

Fishery employmentin the portGeneral

employmentin the areaFleet in each port

from fleet register

Accessibility analysis

Reference employment from high resolution population map

Geo coding of fishing ports from fleet register

Fishery employment model

+

Fig. 1. Outline of the methodology. Detailed information from the fleet register is used to estimate fishery employment at each fishing port disaggregating national figuresavailable through the EU Data Collection Framework. Fishing Service Areas are defined using the accessibility analysis to fishing ports. The reference employment iscalculated extracting population counts in the Fishing Service Areas from a high resolution population map for Europe. Finally the relevance of fishing ports is determined asa ratio between the estimated fishery employment and reference employment in the respective Fishing Service Area.

F. Natale et al. / Marine Policy 42 (2013) 245–252248

is particularly strong for small and medium sized vessels andbecoming weaker as vessel size increases.

In 2010, employment in the fishery sector determined by themodel (Eq. (1)) for the geo coded ports was estimated at 122,300jobs, representing 82% of the jobs estimated for the total EUfishing fleet. Of these jobs, 44.1% were associated to vessels below12 m in length, 21.0% to vessels between 12 and 24 m, and 34.8% tovessels above 24 m.

The FSAs covered a total area of 710,253 square km, with anaverage area of 404 square km. In 2010, the population living inthese areas amounted to 110,999,640 people and total employment,considering the active population and unemployment rates, was45,843,529 people. Overall the ratio of employment in the fisheriescatching sector to total employment in the FSAs was 0.26%.

The specific ratio for each FSA was below 1% in 1307 cases,between 1 and 5% in 295 cases, 5–10% in 64 cases and higher than10% in 29 cases (Table 2, Fig. 3 and Fig. 4).

A total of 66,621 jobs in the fishing sector (54.4% of fishing jobs)were generated in ports for which the relevance in respect to thesurrounding FSA in terms of employment was above 1% and22,767 jobs (18.0% of fishing jobs) in ports for which the relevancewas above 5% (Fig. 3).

When looking at coastal communities' dependency on fisheriesemployment over time, the dependency rate has decreasedbetween 2004 and 2010 for the EU average and for most EUcountries (Table 3).

4. Discussion

Previous studies show fisheries employment in relation to theoverall EU economy or by regional and provincial levels while thisstudy focused on the analysis of coastal communities, and hence,on identifying the existence of fisheries dependent areas using amore refined geographical scale. In Saltz and Macfayden [4], thecontribution rate of the catching sector to EU general employment

in 2005 was estimated at 0.09%. The present study indicated anoverall relevance of fisheries employment to general employmentin the surrounding FSA in 2010 of 0.27%. More importantly, therelevance of fishery employment for each port in the EU wascalculated and it was possible to identify specific locations wherethis ratio is particularly high.

The fact that more than 50% of employment generated by thefishing activities is related to areas with a relatively high depen-dency rate above 1% is supporting the argument of adopting a localapproach in considering socio economic impacts from fisheryrelated policies.

The increase in the importance of the fisheries sector ingenerating employment in respect of previous studies is not dueto an increase of the fisheries sector importance over time but onthe use of a more precise geographical scale. In fact, the signifi-cance of fisheries as a generator of employment in coastalcommunities has decreased over the period analysed (Table 3).

Fig. 5 shows the geographical distribution of fishery dependentcommunities, where high dependency ratios appear to predomi-nate in the northern EU countries although, one would initiallyexpect southern European (Mediterranean) communities to bemore dependent on fisheries. This can be explained by theexistence of large populated areas along the Mediterranean coasttogether with good transport infrastructures implying that mostfishing ports are not remotely located from populated centres.

Since fishery employment is estimated from the fleet structureit is possible to assess the relevance of fishing ports separately foreach vessel length category and, in this way, to consider localimpacts deriving from the different types of employments. Theseresults can be used to monitor and analyse the impact of differentmanagement measures on coastal communities, and in particular,those with a high dependency on the fisheries sector. For example,if a hypothetic introduction of ITQs (or some sort of individualproperty rights) would lead to a concentration in the sector, andconsequently a reduction of the artisanal fleet, this will affectcoastal communities to various degrees depending on the

share of trips within 10 km from port of registration

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Fig. 2. Activity patterns for a sample of vessels from one MS during a year from the fleet register, and trips from log book data. Results show that most vessel have 100% oftrips leaving or arriving at the port of registration (upper left), and that most of the trips (lower left), nr of landings (upper right) and to lesser extend the value of landings(lower right) are linked either to the port of registration or other ports within a few km from the port of registration.

Table 2Number of ports by fisheries dependency by country in 2010.

Country Number of ports o1% 1–5% 5–10% 410%

Belgium 4 4 – – –

Bulgaria 12 3 6 3 –

Cyprus 8 7 – 1 –

Denmark 252 218 25 6 2Estonia 95 50 28 11 6Finland 6 2 4 – –

France 35 28 7 – –

Germany 147 127 16 3 1Greece 102 39 44 13 6Ireland 11 5 5 1 –

Italy 266 218 39 6 3Latvia 8 5 3 –

Lithuania 1 1 – – –

Malta 27 21 5 1 –

Netherlands 19 16 3 – –

Poland 23 20 3 – –

Portugal 26 18 6 2 –

Romania 6 4 1 – 1Slovenia 2 2 – – –

Spain 256 191 55 7 3Sweden 285 244 31 5 4United Kingdom 106 89 12 2 3Total 1697 1307 295 64 29

F. Natale et al. / Marine Policy 42 (2013) 245–252 249

importance of the artisanal fleet in generating employment in agiven community.

The fact that in some countries the number of fishing jobsreported in fishery statistics is lower than the number of fishingvessels registered as active is a sign that the fishing fleet is in manycases underutilised or that fisheries related employment is overreported. The model which links employment from official statis-tics and the number of vessels in the fleet register takes intoaccount a potential average underutilisation rate across MSs, yearsand fleet segments but is not able to capture differences infunction of fishing effort and specific fisheries characteristics suchas type of gear used and main species targeted. Therefore,employment figures for the fishing sector estimated in this study

have to be considered in terms of average employment rather thanactual employment linked to specific levels and types of fishingactivity. Further research may be foreseen to refine the model onthe basis of disaggregated data and to link employment with thespecific characteristics of the fisheries, their level of activity andother economic variables.

The delineation of FSAs is based on accessibility analysis using afixed threshold of 25 min of commuting time instead of referringto distance or administrative units. This offered a more realisticrepresentation of local features and allowed to capture the specificgeographical characteristics determining remoteness and isolationwhich are particularly relevant in the case of fishing ports locatedon small islands and in remote rural areas.

By simplifying the underlying idea behind gravity models, theFSAs represent accessibility boundaries, which indicate the existenceof potential employment flows towards job opportunities at fishingports. This is a simplification of gravity models in two respects:distance is not considered as a continuous variable, and the relativeimportance of origin (populated areas) and destination (fishing ports)are not explicitly used to quantify the intensity of employment flowsfor all possible combinations of origins and destinations.

Instead of comparing fisheries employment with generalemployment in an administrative region, the FSAs allowed toconsider employment in a closer surrounding and to reflect morerealistically geographical features, accessibility and the tendencyfor people to gravitate towards job opportunities close to theirplace of residence.

With this approach the importance of fishing ports in fisheriesdependent communities is emphasised by the presence of a largenumber of fishing vessels and by the presence of surroundingareas sparsely populated, with high unemployment rates and pooraccessibility. All these factors correspond to categories applied inthe definition of disadvantaged areas in regional policies.

In the previous study by Eurostat [12], the Maritime Service Areaswere overlaid with local administrative units and the relevance ofcoastal activities was assessed for these units in terms of thepercentage of surface area falling in the Maritime Service Areas.Population affected by the Maritime Service Areas was then

0−1% 1−5% 5−10% 10−20% >20%Ratio between fisheries and general employment

Num

ber o

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hing

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as

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0−1% 1−5% 5−10% 10−20% >20%Ratio between fisheries and general employment

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Fig. 3. Number of fishing ports (left) and employment in the fisheries sector (right) by dependency rate in the surrounding Fishing Service Areas. Around 53% of fishing jobsare in areas where fishery employment represents more than 1% of total employment.

10 20 50 100 200 500 1000 200050

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Fig. 4. Fishing ports with more than 10 person employed in the fishery sector and where fisheries employment represents more than 10% of total employment in thesurrounding areas. Local communities around these fishing ports can be considered highly dependent on fishing activities.

F. Natale et al. / Marine Policy 42 (2013) 245–252250

estimated proportionally to the overlapping surface consideringuniform density distributions from census data. In this study theperspective is reversed and what is measured is the relevance of eachFSA on the basis of employment generated at fishing ports incomparison to the general employment gravitating in the FSA. Thisreversed perspective represents a shift from an administrativereference when considering the statistical and economic relevanceto a geographical one. Instead of using predefined geometries basedon administrative boundaries, areas of influence are drawn consider-ing accessibility and the reference population is captured using amore precise disaggregation method based on land cover ancillaryinformation rather than a uniform density distribution.

A basic assumption in this study is that the port of registrationin the fleet register corresponds to the place where employmentgenerated from fishing activity can be attributed. Considering the

spatial patterns of trips and landings examined for a sample ofvessels this assumption can be considered valid for small andmedium size vessels. The relevance of such linkage goes behindthe present study on fishery employment since it provides thepossibility to disaggregate spatially also other socio-economic dataavailable at EU level.

5. Conclusions

The innovation of this study lies in the adoption of spatialmethods and in taking a geographical perspective rather thanadministrative for estimating the contribution of fisheries to localeconomies at an EU-wide scale. This approach allowed to identifyand map specific local communities in which, given the conditions

Table 3Evolution of the fisheries employment dependency ratio by country.

Country 2004 2005 2006 2007 2008 2009 2010

Belgium 0.524 0.507 0.490 0.424 0.408 0.403 0.326Bulgaria – – – – 0.642 0.669 0.766Cyprus – 0.632 0.536 0.540 0.812 0.982 1.142Denmark 0.042 0.049 0.056 0.072 0.089 0.100 0.120Estonia 1.009 0.905 0.769 0.715 0.752 0.404Finland 0.580 0.550 0.507 0.476 0.473 0.496 0.507France 0.419 0.412 0.407 0.379 0.369 0.347 0.329Germany 0.181 0.169 0.157 0.151 0.141 0.137 0.131Greece 0.604 0.591 0.568 0.553 0.529 0.531 0.532Ireland 0.494 0.441 0.417 0.423 0.443 0.428 0.439Italy 0.406 0.397 0.382 0.373 0.366 0.371 0.372Latvia – 2.235 1.974 1.763 1.693 1.861 1.631Lithuania – 0.047 0.032 0.076 0.092 0.081 0.062Malta – 0.862 0.855 0.828 0.819 0.703 0.703Netherlands 0.118 0.119 0.106 0.102 0.100 0.090 0.094Poland – 0.441 0.301 0.263 0.233 0.207 0.172Portugal 0.519 0.519 0.329 0.312 0.304 0.299 0.280Romania – – – 0.018 0.074 0.063 0.096Slovenia – 0.318 0.313 0.328 0.333 0.346 0.348Spain 0.442 0.408 0.378 0.363 0.356 0.363 0.347Sweden 0.112 0.115 0.107 0.108 0.110 0.104 0.099United Kingdom 0.138 0.131 0.124 0.120 0.115 0.111 0.109Average EU Total 0.315 0.312 0.288 0.278 0.274 0.272 0.267

Fig. 5. Map of EU fisheries coastal communities. Communities with employmentdependency ratios above 5% are plotted with proportional symbols.

F. Natale et al. / Marine Policy 42 (2013) 245–252 251

of accessibility, employment and size of the fishing fleet, thedependence on fishing activities can be considered particularlyrelevant, i.e. with ratios above 5%.

Monitoring these communities would help to analyse thesocio-economic effects of different fishery policies in these parti-cularly sensitive areas. Furthermore, these communities meritspecific policy actions aimed at preserving job opportunities inthe fishing sector as underlined by the undergoing reform of theEU Common Fishery Policy. Delineating fisheries dependent com-munities provides a valuable input for strategic planning andimproved economic allocation of management measures. Thecurrent and future EU policies are committed to minimising theeconomic impacts and improving employment in fisheries in thesecommunities. These legal and policy instruments targeted atfisheries dependent communities require reliable and updatedinformation to implement effective management measures forsustainable fisheries development. This information is available forthe entire EU only at a high level of aggregation; however the EUfleet register containing detailed data on individual vessels at eachport, provides the mean to disaggregate spatially socio-economicvariables and to perform studies at local scale.

By evaluating estimated fishery employment at each port inreference to general employment conditions in the surroundingaccessibility areas two objectives are pursued: the first is toconduct the analysis of dependency at more refined geographicalscale while ensuring an EU coverage, and the second is todetermine reference communities not on the basis of arbitraryadministrative boundaries but mirroring the socio-economic phe-nomena at study which was is our case the flow of people towardsjob opportunities.

While case studies would be able to explore more in detail thesocio economic aspects of fisheries at local level, the presentedanalysis provides a method for identifying fisheries dependentcommunities using data already collected and complied for theentire EU. The analysis can be broadened to include other relevanteconomic data on theses fishing communities to provide a moredetailed tool for informed policy decisions.

References

[1] European Commission. Reform of the Common Fishery Policy COM (2011) 417;2011.

[2] Teh L, Sumaila U. Contribution of marine fisheries to worldwide employment.Fish and Fisheries 2011;14(1):77–88.

[3] Goulding I, Hallamm D, Harrison-Mayfield L, Mackenzie-Hill V, da Silva H.Regional socio-economic studies on employment and the level of dependencyon fishing. Lot No.23: Coordination and Consolidation Study, 1999 113.

[4] Salz P, Macfadyen G. Regional dependency on fisheries. IP/B/PECH/ST/IC/2006-198 2007; ⟨http://www.framian.nl/our_publications/⟩; 2012 [accessed Septem-ber 2012]: 162.

[5] Macfadyen G, Salz P, Cappell R. Characteristics of small-scale coastal fisheriesin Europe. IP/B/PECH/IC/2010-158; 2011.

[6] European Commission. Second report on economic and social cohesion; 2001,⟨http://ec.europa.eu/regional_policy/sources/docoffic/official/repor_en.htm⟩;2012 [accessed September 2012].

[7] MRAG, Poseidon, IFM, Oceanic, IEEP, Lamans. Regional social and economicimpacts of change in fisheries-dependent communities. Studies in the Field ofthe Common Fisheries Policy and Maritime Affairs, Lot 4 Impact AssessmentStudies related to the CFP 2011; DG MARE FISH 2006/06.

[8] European Commission. Regulation (EC) no. 665/2008. Official Journal; 2008, L186/3.

[9] European Commission. Fifth report on economic, social and territorial cohe-sion; 2012, ⟨http://ec.europa.eu/regional_policy/sources/docoffic/official/repor_en.htm⟩; 2012 [accessed September 2012].

[10] Huff DL. A probabilistic analysis of shopping center trade areas. LandEconomics 1963;39:81–90.

[11] Anderson J. The gravity model. Annual Review of Economics 2011;3:133–60.

F. Natale et al. / Marine Policy 42 (2013) 245–252252

[12] Trifonov A, Willadsen T, Collet I. Maritime service areas. Highlighting theimpact of coastal maritime activities on the hinterland. Eurostat Statistics inFocus 2011:41.

[13] Gallego F, Batista F, Rocha C, Mubareka S. Disaggregating population density ofthe European Union with CORINE land cover. International Journal of Geo-graphical Information Science 2011;25(12):2051–69.

[14] European Commission DG MARE. Community Fishing Fleet Register; 2012,⟨http://ec.europa.eu/fisheries/fleet/index.cfm⟩; 2012 [accessed March 2012].

[15] STECF. The 2011 Annual Economic Report on the EU Fishing Fleet. STECF-11-16; 2011 233.

[16] Batista eSilva F, Gallego FJ, Lavalle C. A high resolution population grid map forEurope. Journal of Maps 2013. http://dx.doi.org/10.1080/17445647.2013.764830.