Economics of the marine: modelling natural resources 1783485590, 9781783485598

Table of contents :
Cover
Half Title
Title Page
Copyright Page
Dedication
Contents
Chapter One Introducing the Marine Economy
1.1 Introduction
1.2 A Note on Nomenclature
1.3 The Marine Resource and Economic Activity
1.4 What Is the Marine Economy?
Measuring the Marine Economy: Empirically Defining the Marine Sector
Measuring the Marine Economy: Scope of the Marine Sector
1.5 Measuring the Marine Economy
Data
Data Types
Methodology
1.6 International Trends
1.7 Conclusions
Bibliography
Chapter Two The Marine Economy: A National Perspective
2.1 Introduction
2.2 Linkages
2.3 Input–Output Analysis
2.4 Case Study: The Irish Marine Economy, An Input–.Output Analysis
Case Study: Data Requirements
Case Study: Linkages Within the Marine Sector
Case Study: Forward linkages
Production-Inducing Effects of the Irish Marine Sector
Employment-Inducing Effects of the Marine Sector
Case Study: Discussion
2.5 Conclusions
Bibliography
Chapter Three Accounting the Marine Economy: Capturing Economic Change Through Time Series Data
3.1 Introduction
3.2 Data for Economic Trend Analysis
3.3 Case Study: Trends in the English Marine Sector: 2003 to 2011
3.4 Results
3.5 Using Trend Data on the Marine Economy for Policy and Governance
3.6 Conclusions
Bibliography
Chapter Four The Marine Sector and the Regions
4.1 Introduction
4.2 The Geography of the Marine Economy
4.3 Case Study: The Marine Economy and the Irish Regions
The Role of the Marine Economy in the Irish Regions
The Irish Regional Marine Economy: Labor Market Indicators
The Irish Regional Economy: Productivity Market Indicators
4.4 Discussion
4.5 Conclusions
Bibliography
Chapter Five The Economic Impact of the Marine Sector on the Regions: A Location Quotient Approach
5.1 Introduction
5.2 Location Quotients
5.3 Methodology
Location Quotients
5.4 Case Study: Ireland
Discussion of the Irish Case Study
5.5 Conclusions
Bibliography
Chapter Six Regional Development and Marine Clusters
6.1 Introduction
6.2 Clusters and the Marine Economy
Maritime Clusters
6.3 Case Study: An Irish Maritime Cluster
Location Quotients
Linkages Within the Irish Maritime Sector
Linkages Within the Maritime Transportation Sector
Forward Linkages
Case Study: Discussion
6.4 Discussion
Bibliography
Chapter Seven Marine Clusters: Specialization or Diversity?
7.1 Introduction
7.2 Related Variety
7.3 Maritime Clusters and the Irish Maritime and Energy Resource Cluster (IMERC)
7.4 Methods
7.5 Results
The Four IMERC Pillars and the Wider Economy
Relatedness Among the IMERC Pillars
7.6 Discussion
Bibliography
Chapter Eight From National to Regional to Local: A Spatial Microsimulation Model for the Marine
8.1 Introduction
8.2 Spatial Microsimulation
8.3 Simulation Model of the Irish Local Economy (SMILE)
Quota Sampling (QS)
Calibration
SMILE Marine
8.4 Data
8.5 Results
National-Level Analysis
Marine Employment Contribution at the County Level
Marine Income Contribution at the County Level
Income Spread
8.6 Discussion
Bibliography
Chapter Nine The Marine Sector: A Panacea in Peripheral, Deprived Areas?
9.1 Introduction
9.2 Data
Business Structure Dataset
Geographical References Within the BSD
Socio-Economic Data
Index of Multiple Deprivation for England 2010
9.3 Results
Fishing Sector
9.4 Discussion
9.5 Conclusions
Bibliography
Chapter Ten Conclusions
Bibliography
Index

Citation preview

 

Economics of the Marine

 

 

Economics of the Marine Modelling Natural Resources

Karyn Morrissey

London • New York

 

Published by Rowman & Littlefield International Ltd Unit A, Whitacre Mews, 26-​34 Stannary Street, London SE11 4AB www.rowmaninternational.com Rowman & Littlefield International Ltd.is an affiliate of Rowman & Littlefield 4501 Forbes Boulevard, Suite 200, Lanham, Maryland 20706, USA With additional offices in Boulder, New York, Toronto (Canada), and Plymouth (UK) www.rowman.com Copyright © 2017 by Karyn Morrissey All rights reserved. No part of this book may be reproduced in any form or by any electronic or mechanical means, including information storage and retrieval systems, without written permission from the publisher, except by a reviewer who may quote passages in a review. British Library Cataloguing in Publication Data A catalogue record for this book is available from the British Library ISBN:  HB: 978-​1-​78348-​558-​1 PB: 978-​1-​78348-​559-​8 Library of Congress Cataloging-​in-​Publication Data Available ISBN: 978-1-78348-558-1 (cloth : alk. paper) ISBN: 978-1-78348-559-8 (paper : alk. paper) ISBN: 978-1-78348-560-4 (electronic) The paper used in this publication meets the minimum requirements of American National Standard for Information Sciences—​Permanence of Paper for Printed Library Materials, ANSI/​NISO Z39.48-​1992. Printed in the United States of America

 

In dear memory of my Nan Josephine O’Dea.

 

 

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Contents

1 Introducing the Marine Economy

1

2 The Marine Economy: A National Perspective

19

3 Accounting the Marine Economy: Capturing Economic Change Through Time Series Data

43

4 The Marine Sector and the Regions

59

5 The Economic Impact of the Marine Sector on the Regions: A Location Quotient Approach

75

6 Regional Development and Marine Clusters

89

7 Marine Clusters: Specialization or Diversity?

107

8 From National to Regional to Local: A Spatial Microsimulation Model for the Marine

125

9 The Marine Sector: A Panacea in Peripheral, Deprived Areas?

145

10  Conclusions

159

Index

161

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Chapter One

Introducing the Marine Economy

1.1  INTRODUCTION Human activities in the world’s oceans and coasts are at an unprecedented scale and expanding rapidly (Stojanovic & Farmer, 2013). The oceans have become a focal point for new activities including wind and wave power, marine biotechnology, marine technology and other enterprises (Kildow & McIlgorm, 2010; Morrissey et al., 2011; Zhao et al., 2013). In response, concerns over the impact of economic activities on the marine resource have led to many national ocean policies and international agreements for sustainable marine development. A history of single-​sector management of marine activities with little engagement with relevant communities and stakeholders means that an estimated 60 percent of the world’s major marine ecosystems have been degraded or are being used unsustainably (UNEP, 2011). Indeed, there are many indicators that suggest we are failing to effectively regulate and conserve vital ocean-​based resources, and that this failure will potentially lead to consequences that include ecosystem tipping points, or dramatic shifts in structure and function that are hard to reverse (Selkoe et al., 2015). Within this context, coastal and marine policy makers and managers have become increasing aware of the need to support and analysis the economic and social dimension of marine activity. However, economic data on the activities linked to the marine resource is often incomplete or nonexistent (Kildow & McIlgorm, 2010). This is mainly due to the difficulties in empirically measuring a multisector resource such as the marine. The fragmented, multisectoral nature of the marine economy and the difficulty in distinguishing between land-​and marine-​based activities has meant that national economic datasets do not explicitly contain a marine sector. Historically, estimates of the value of the marine resource have been 1

 

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unable to provide a holistic value for the sector. However, given the increased impetus on marine spatial planning for commercial and environmental sustainability, regulation in areas such as fisheries, marine energy, and aquaculture, and economic information to aid governmental prioritization, governments require a range of economic indicators for the sector (Kalaydjian, 2016). These indicators may be then used to develop new policy measures to facilitate the sustainable development of the resource and its commercial activity. Within this context, recent international research has begun to conceptualize the marine economy as a multi-​sector industry. This book extends the current international interest in the conceptualization of a national marine sector to explore the importance of the sector at the national, regional and local level using base theory, New Economic Geography, agglomeration theory, industrial cluster policy and small area level analysis. In conjunction with each spatial scale and its associated theories, a number of computational methods will be used to explore the economic impact of the marine resource. Input–​output tables will be used to demonstrate how the direct and indirect economic impact of the marine sector may be measured at the national level. Location Quotients will be used to regionalize these input–​output tables, allowing a regional level analysis of the importance of the marine sector. Finally, a spatial microsimulation model will be used to examine the impact of the sector at the small area level. In a time, where societal impact is increasingly important, this book is of interest to policy makers, both academic and planner practitioners, physical scientists interested in estimating the impact of research on society and the wider social sciences including geography and sociology. In engaging a wide audience this book also aims to bridge some of the gaps encountered by those carrying out inter-​and multidisciplinary research by conceptualizing the marine as a commercial resource that requires management and planning. This book aims to engage academics, professionals and policy makers on the importance of the marine resource to society. 1.2  A NOTE ON NOMENCLATURE To begin this book, I believe it is important to offer a note on the current nomenclature associated with oceans and their economy. The terminology related to the ocean economy is used differently around the world and include such terms as “ocean industry,” “marine economy,” “marine industry,” “marine activity,” and “maritime sector” (Park & Kildow, 2014). More recently the economic activities associated with our oceans and seas have also been branded as the “Blue Economy” with both Europe and China keen to develop their ocean resources. The emergence of the “Blue Economy” brand

 



Introducing the Marine Economy

3

also coincides with the increased emphasis on “greening” the economy. By virtue of its color, the Blue Economy has somehow become synonymous with the sustainable development of the world’s oceans and seas. Whether this branding will actually result in a more sustainable marine economy is up for debate; however, such a debate true is not the objective of this book. For the sake of consistency, I predominately use the term the marine economy to describe the economic activities associated with our ocean and seas. In an unusual display of pedanticism I prefer the “marine economy” as it encompasses both ocean-​and sea-​based resources, rather than using the ocean as a reference to both. However, I agree with Park and Kildow (2014) in their statement that it is not necessary to overly distinguish among these terms because they are interchangeable among the nations. However, I do note that for the purpose of this book and my own personal research, I do distinguish “maritime activity” from “ocean” and “marine” activity because in most languages and international institutions the term “maritime” refers primarily to shipping activity. A second note on the use of the word “economy” throughout this book is also necessary. All the marine goods and services examined in this book are exchanged in the marketplace with individuals expressing their preferences via their individual purchasing behavior. The price an individual pays reflects how much, or at the very least what, they are willing to pay for the benefits they derive from consuming that marine good or service. However, many of the ecosystem goods and services, provided by marine resources, are not traded in actual markets. In these cases market price data are missing even though these ecosystem goods and services generate significant nonmarket or external benefits. Thus, there is an automatic underestimation of the value of our oceans and seas by only calculating the value of commercial marine activities. Methods of economic valuation also provide several tools that may be employed to value benefits that are derived from marine nonmarket goods and services. To provide a true valuation of our oceans and seas, economic appraisals of the marine sector should be supported by nonmarket valuations of ecosystems goods and services. Methodologies for calculating nonmarket values of ecosystems and services are still in their infancy, but are being actively encouraged under the new EU Directives and Policies, e.g. European Marine Strategy Framework Directive and guidelines for Maritime Spatial Planning. Thus, a limitation of this book is its sole focus on marine commercial activities as a proxy for the economic value of the marine resource. 1.3  THE MARINE RESOURCE AND ECONOMIC ACTIVITY The ocean has attracted multiple economic use for centuries, with fisheries, oil and gas extraction, shipping and transportation, the military, mining,

 

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recreation, and conservation, among others. Many areas attract a variety of competing uses, which overlap and cause conflicts between users (user–​ user conflicts) and users and the environment (user–​environment conflicts) (Cicin-​Sain & Knecht, 1998). However, compared to other natural resource sectors such as agriculture, mining or energy, research on the economic value of the marine sector has been limited to date. This is mainly due to the difficulties in empirically measuring a multisector resource such as the marine (Peng et al., 2006; Morrissey et al., 2011) and the widespread belief that the marine sector is a low-​skilled, peripheral activity characterized by the seafood sector. In reality, this has meant that the true commercial value of the marine resource has been ignored within the policy arena. The realization, however, that land activities have reached their limits and the availability of new technologies that allow for greater interaction with the sea (Morrissey et al., 2011; Stojanovic & Farmer, 2013; Zhao et al., 2014), the future wide-​ scale exploitation of the marine resource is increasingly inevitable (Ellis & Flannery, 2016). Suddenly, the few social scientists working on anything that constituted an ocean-​ based activity, along with marine biologists, ocean chemists and physicists were called on to build an evidence base from which the plethora of ocean activities could be developed in a sustainable manner. In response, a large body of research across the social sciences has begun to document and map this exploitation, including research in planning (Douvere & Ehler, 2009; Flannery & Cinnéide, 2008; O’Mahony et al., 2009), governance (Evans et al., 2011; Flannery & et al., 2016), citizenship (Fletcher & Potts, 2007; McKinley & Fletcher, 2012), and economics (Kildow & McIlgorm, 2010; Morrissey et al., 2011; Colgan, 2013). In response to this increase focus on the social, economic, and cultural aspects of the marine environment, international journals such as Marine Policy, Coastal and Ocean Management and the Journal of Ocean and Coastal Management publish papers on all aspects of the oceans and new journals such as the Journal of Ocean and Coastal Economics further increase the platforms in which research on the social aspects of the marine can be disseminated. While marine management as a policy framework has always contained an element of concern about the type and level of economic activity associated with the use of ocean resources, to date the information needs of policy makers and managers has focused on data about the marine resource itself rather than the economic environment in which it is used (Colgan, 2013). However, given the increased impetus on marine spatial planning for commercial and environmental sustainability (Douvere, 2008; Flannery & O’Cinneide, 2008; O’Mahony et al., 2009), regulation in areas such as

 



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5

fisheries, marine energy, and aquaculture (Colgan, 2013), the obvious failure of single-​sector marine policies to achieve sustainable resource use (Böhnke-​Henrich et al., 2013) means that economic and social data are recognized as indispensable to the management and conservation of the marine resource (Ban et al., 2013; Koehn et al., 2013; Jin et al., 2013). These indicators may be then used to develop new policy measures to facilitate the sustainable development of the resource and its commercial activity (Morrissey, 2015; Colgan, 2013). This chapter focuses on defining the marine commercial sector and the methods used to value the marine economy at the national level. Later chapters will examine the impact of the marine sector at the regional and local level. The chapter continues as follows: Section 1.2 begins to conceptualize the marine sector as commercial economic activity that derives all or part of its inputs from the ocean and examines international definitions of the marine sector to date. Section 1.3 outlines the data required to estimate the national value of the marine sector. Section 1.4 presents a methodology that may be used to estimate the value of the marine sector at the national level. Section 1.5 presents the most up-​to-​date estimates of previous international studies. Finally, section 1.6 discusses the results and points to the policy relevance of such data. 1.4  WHAT IS THE MARINE ECONOMY? As the value of the marine resource in terms of both environmental and commercial sustainability has become increasingly apparent, policy makers and governments both regional and national are increasingly interested in placing an economic value on the world’s ocean and sea resource. However, research on the economic value of the marine sector has been limited to date (Colgan, 2003; Kildow & McIlgorm, 2010). This is mainly due to the: a. Difficulties in empirically defining the marine economy and b. Limitations in national accounts data (Talento, 2016; De Maio & Irwin, 2016) c. Lack of institutional commitment to producing data on the marine economy (McIlgorm, 2016) d. Belief that the marine economy is synonymous with the fisheries sector The reminder of this chapter (and indeed book) focuses on how countries have begun to reconceptualize their marine resources to produce a coherent and consistent assessment of their marine economy.

 

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Measuring the Marine Economy: Empirically Defining the Marine Sector Initial studies were largely based in North America (Nathan Associates, 1974; Pontecorvo et al., 1980; Pontecorvo, 1988) and varied from state (US, Moeller & Fitz, 1994) and province level (Canada, Mandale et al., 1998, 2000) analysis to the first international study provided by (Colgan & Plumstead, 1993). These studies tended to rely on employment in specific industries for which economic data already existed and focused on the marine activities that were most easily measured, including fishing and maritime transport. Other studies focused attention on the coast rather than the ocean. Luger developed a methodology for measuring coast-​dependent, coast-​linked, and coastal-​ service activities (Luger, 1991). While this approach significantly expanded the types of economic activities brought into the measurement process, not all coastal activities are marine-​based activities. In an effort to overcome these limitations, Colgan (2003) introduced the concept of “ocean GDP” which he defined as “the economic activities and industries that utilize ocean resources in a production process.” Within this definition, the marine sector is conceptualized as any good or service that directly or indirectly use the marine resource within their process of production. Thus, according to definition, marine-​based industries are based on two broad categories (Colgan, 2003): • Industries that involve the direct use of marine resources ○○ Marine food, transportation and energy industries, • Industries that create value through the provision of products and/​or services indirectly associated with the marine environment ○○ Seafood processing, marine tourism, marine commerce, and blue biotechnology. A more recent piece of research by Park and Kildow (2014) further define the marine economy as: 1 . Activities that explore and develop ocean resources 2. Activities that use ocean space 3. Activities that protect the ocean environment 4. Activities that use ocean products as a main input 5. Activities that provide goods and services to ocean activities Where activities 1–​3 are the activities that take place in the ocean and (4) and (5) are the activities that support the ocean activities or are derived from them. Regarding the geographical scope of the marine sector, Colgan

 



Introducing the Marine Economy

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Figure  1.1.  Comparing the Ocean and Coastal Economies.  Source:  Kildow & McIlgorm, 2010.

(1997) notes two important concepts; the marine economy and the coastal economy (­figure  1.1). The marine economy includes all enterprises that “derive all or part of their inputs from the ocean” (Colgan, 1997; Kildow & McIlgorm, 2010). In contrast, the coastal economy is the portion of the national economy which takes place on territory adjacent to the sea, regardless of whether it is marine-​related or not. Thus, as Kildow and McIlgorm (2010) point out the terms “coastal” and “marine” economy are not synonymous and the marine economy is considerably smaller than the coastal economy. Measuring the Marine Economy: Scope of the Marine Sector Currently no standard definition of what constitutes a national marine sector currently exists (Morrissey et al., 2011; OECD, 2016); however v­ arious researchers and countries have undertaken a lot of effort over the past decade in defining and measuring the ocean economy (De Maio & Irwin, 2016). These include studies (table 1.1) in France (Kalaydjian et al., 2009, Girard and Kalaydjian, 2014), the United States (Kildow & McIlgoem, 2010), the UK (Pugh, 2008; Morrissey, 2015), Australia (Allen Consulting, 2004), Ireland (Morrissey et al., 2011; Vega et al., 2013), China (Zhao et al., 2014), Canada (RASCL, 2003), New Zealand (Statistics NZ, 2006), Japan (Nakahara, 2009), and South Korea (Hwang et al., 2011; Chang Jeong, 2015). Generally speaking each of these studies has used Colgan (2003) concept of Ocean GDP and incorporate the difference between marine and coastal activities. However, important definitional differences exist among these studies (table 1.2). In some instances, this is due to data limitations and researchers using the best available data (Morrissey, 2015), the sector may not exist in that country (aggregate mining is not an established sector in

 

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Table 1.1  Previous International Estimates of the Marine Economy Country report

Latest year

Marine sector GDP/​GVA

% of national GDP

Australia (Allen Consulting, 2004) Canada (Gardner Pinfold) France (Girard & Kalaydjian 2014) New Zealand (Statistics NZ, 2006) UK (Pugh, 2008) USA(Kildow et al., 2009) China (Zhao et al., 2014) Ireland (Vega et al., 2013) South Korea (Hwang et al., 2011) Japan (Nakahara, 2009) Indonesia (Nurkholis et al., 2016)

2003 2006 2013 2002 2006 2004 2010 2012 2008 2005 2010

Au$15bn Cn$17.7bn €21.5bn NZ$3.3bn €46bn US$138bn CNY4,557BN €1.3bn KRW13,435bn JPY7,863bn

3.6 1.2 1.20 2.90 4.20 1.2 9.6 0.7 4.9 1.6 7.6

Ireland) or the rapid advancements in technology (blue biotechnology is an emerging sector and earlier studies would not have recognized this sector (OECD, 2016). A study by Park and Kildow (2014) provides an excellent overview of how the definition of the marine economy varies from country to country (table 1.2). Park and Kildow (2014) note that Ireland’s definition of the marine economy draws on the methodology devised by Colgan (1997, 2003) for the National Ocean Economics Program (NOEP) in the United States and that the UK’s definition is also similar to that of the USA and Ireland. Australia defines the ocean economy as an “ocean-​based activity” and focuses on whether the ocean resource is the main input or not. New Zealand defines the ocean economy as the economic activities that take place in the ocean, or use the marine environment, or produce goods and services necessary for those activities, or make a direct contribution to the national economy (Park & Kildow, 2014). With regard to the Asian economies (Park & Kildow, 2014) note that China defines the ocean economy as the “sum of all kinds of activities associated with the development, utilization and protection of the marine environment.” Japan defines it as the “industry exclusively responsible for the development, use, and conservation of the ocean.” Finally, South Korea defines the ocean economy as the “economic activity that takes place in the ocean, puts goods and services into ocean activity, and the activity that uses the ocean resources as an input (Park & Kildow, 2014). However, each country reports oil and gas, maritime transportation, port and shipping logistics, boat building, marine tourism, fishing and aquaculture as part of their marine economy, while with the exception of Australia all other countries define marine construction as part of the marine

 



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Table 1.2  Definition of the Marine Economy by Country (Park & Kildow, 2014) Country

Definition

USA

The activity, which is (a) an industry who definition explicitly ties the activity to the ocean, or (b) which is partially related to the ocean and is located in a shore adjacent zip code Those activities which involve working on or in the sea. Also those activities that are involved in the production of goods or the provision of services that will directly contribute to activities on or in the sea. Ocean-​based activity (“Is the ocean resource the main input? Is access to the ocean a significant factor in the activity”) Economic activity which directly or indirectly uses the sea as an input The sum of all kinds of activities associated with the development, utilization and protection of the marine. Those industries that are based in Canada’s maritime zones and coastal communities adjoining these zones, or are dependent on activities in these areas for their income The economic activity that takes place in, or uses the marine environment, or produces goods and services necessary for those activities, or makes a direct contribution to the national economy Industry exclusively responsible for the development, use and conservation of the ocean The economic activity that takes place in the ocean, which also includes the economic activity, which puts the goods and services into ocean activity and uses the ocean resources as an input.

UK

Australia Ireland China Canada

New Zealand

Japan South Korea

economy (table 1.3). The studies provided by France, Canada, and the UK also include noncommercial activities as part of the marine economy. These three countries report marine-​related defence and government activity and marine-​based research and education, while France also reports economic activities related to coastal and marine environmental protection. Further classifications have classified the marine sectors into natural resource, services and manufacturing (Morrissey et al., 2011; Vega et al., 2015) and traditional and emerging marine sectors (Morrissey et al., 2011; Zhao et al., 2014; Vega et al., 2015, OECD, 2016). While France divides marine economic activity into industrial (commercial) and noncommercial activities (Kalaydian et al., 2008; Girard & Kalaydjian, 2014; Park & Kildow, 2014). Interestingly, South Korea divides its marine economy into three categories: marine-​based industry, forward marine-​related industries (industries that provide inputs to the marine sector), and backward marine-​related industries (industries that use marine sector outputs) (Park & Kildow, 2014). This classification and terminology is similar to that of an input–​output table. Input–​output tables will be discussed further in ­chapter  2.

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Table 1.3  The Industries Defined as Part of the Marine Economy within International Studies Ireland

UK

USA

France

New Zealand

China

South Korea

Japan

Canada

X X

X X

X X

X X

X X

X X

X X

X X

X X

X X

X X X X

X

X X X X X X X X X

X

X X X X X X X X X

X X X X

X X

X

X X X X X

X

X X X X X X X X X X X X X X X X

X

X X X X X X

X X X X X X X X X X X X X X X X X

X X

n/​a

X X X X X X X X X X X

X X

X X

X X

X X

X X X

X X X X

X X X

X X

Indonesia X

X X X X X X

X X X

X X X

X

X

X

X X X X

X X

X X

X

X

X X

Chapter One

Maritime transport Port and maritime logistics Tourism High-​tech services Commerce Other services Aggregates Fisheries Aquaculture Seafood processing Seaweed Biotechnology Oil and Gas Renewable energy Boat building Construction Engineering Manufacturing Seawater utilization Defence/​government Research and education Coastal and marine environmental protection

Australia

 



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1.5  MEASURING THE MARINE ECONOMY Data Government accounts are arguably a proxy for institutional interest in economic activities. While producing detailed national, regional, and in some cases local data on agricultural production, oil and gas extraction and mining, in contrast, no such data exist for the marine sector (Morrissey et al., 2011). At best governments report data on fishing and maritime transport; however, both reside in higher level administrative categories with fishing reported traditionally under agricultural activities and water transport under transportation activities. The multitude of sectors, from recreational and tourism activity to marine renewable energy and biotechnology, dependent on the ocean resource is hidden in a tangle of land-​based activities. Thus, the very essence of these activities, their direct and indirect relationship with our oceans and seas (Colgan, 2003, Kildow & McIlgorm, 2010; Morrissey et al., 2011) remains unaccounted. Thus, as data on the multisectoral marine sector is not available in most National Accounts and due to the diverse number of companies involved in the marine economy data acquisition is “messy” (De Maio & Irwin, 20016), data collection is the most time intensive step in producing an estimate of the marine economy (Morrissey et al., 2011). Data Types To estimate the value of the marine sector a variety of data types or collection methods must be employed (Morrissey et al., 2011). These data types may be broken down into three broad categories: Type 1, Type 2, and Type 3 data (Morrissey et al., 2011). Type 1 data is data that is in the public domain. Such estimates are generally confined to those sectors whose connection to the sea is clear (i.e., commercial fisheries, coastal transportation). Type 2 data is data that is publicly collected but is not released into the public domain. National statistical agencies prepare a number of business censuses and surveys each year. Examples include the Annual Business Survey in the UK or the Census of Production in Ireland. This data is at a lower industrial (such as NACE, Europe; SIC, UK; NAICS, North America) or geographical classification and is considered confidential. However, access can usually be granted to researchers interested in examining the data in a secure setting (Morrissey et al., 2011; Morrissey, 2015). Undertaking an economic valuation of the Irish marine economy Morrissey et al. (2011) note that this included sectors such boat building and oil and gas exploitation. Type 3 data is data that is not available in the public domain. The sectors where there is no publicly available data are sectors that are generally not

 

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easily recognizable as marine based. These sectors are often indistinguishable from their land-​based counterparts within economic datasets. For example, one cannot difference between land-​ based construction and water-​ based construction from the National Accounts data. In some cases, it is possible to obtain data through sector reports and academic research papers, for example the Economic and Social Research Institutes in Ireland had conducted a study on expenditure on water-​based tourism, which Morrissey et al. (2011) used to measure water-​based activities in their economic value of the Irish marine sector (Morrissey et al., 2011). If such data is not available, the researcher has two choices. Limit the valuation of the marine resource to the sectors for which Type 1 or Type 2 data is available (Morrissey et al., 2015) or conduct a survey (Morrissey et al., 2011; Vega et al., 2015). Surveys can be administered face-​to-​face or via post or email. The first step in conducting a survey is to compile a sector-​by-​sector list of companies. An arduous task, in undertaking a survey of marine businesses in Ireland Morrissey et al. (2011) note that the collection of data was facilitated by the fact that many of the companies (particularly the WBA) had to register with national bodies to commence operations and/​or the sector itself was small and well-​defined (for example high-​tech services). Methodology As outlined in section 1.1, estimates of the value of the marine economy are limited due to the definitional and data limitations faced by the sector. Faced with these issues, Colgan (1997, 2003) proposes a number of principles for developing a methodology. • First, data should be comparable across industries and space. For example, the measure for employment in one industry should be the same as in all other industries. This is particularly important when data is being collected and collated across different datasets • Second, data should be comparable across time, so that changes in the sector can be observed and measured. Both Ireland (Morrissey et al., 2011; Vega et al., 2015) and France (Kalaydian et al., 2008; Girard & Kalaydjian, 2014) have produced data on the marine sector on a biannual basis. • Third, the data should be consistent with standard economic theory on the measurement of economic activity. For example, it should not permit double counting of economic activity across sectors, meaning all measures can summed across industries and geography. Based on these principles, Kildow and McIlgorm (2010) propose a five-​ step methodology to estimate the marine economy. These steps include:

 



Introducing the Marine Economy

13

1 . Define the industries that are part of the marine economy 2. Identify publicly available economic data 3. Collect nonpublic data from alternative data sources or a survey 4. Record the economic indicators of interest 5. Ensure consistency of data across different data sources, compile the data and provide sectoral and spatial breakdowns of the value of the marine sector Once all the data has been collected, the next steps involve assessing the performance of the marine economy. The general approach for assessing the performance of a sector relates to standard indicators of production (Colgan, 2003). As standard these indicators include aggregate and/​or sectoral measures of net output/​turnover, inputs, gross value added (GVA) and employment. GVA is a measure comparable to GDP and may be defined as turnover minus input costs. GVA rather than turnover is the preferred indicator for measuring economic activity as it removes the danger of double counting and allows a meaningful comparison across sectors. Other studies have included indicators such as exports (Australia, Allen Consulting, 2004) employee income and productivity (Ireland, Morrissey et al., 2011) of each marine sector. 1.6  INTERNATIONAL TRENDS Traditionally seen as a sector dominated by the seafood industry, the majority of international studies highlighted in table 1.4 found that in six of the studies conducted on the marine economy the marine service sector, particularly maritime transport and marine tourism provided the largest contribution of GDP, GVA, and employment to the marine economy. Oil and Gas were the largest contributors to the UK and Canadian marine economies, while shipbuilding dominated the South Korean marine economy. However, this is not to say that fisheries are not an important sector. The fishing industry was the second largest contributor to the overall marine economy in three countries, New Zealand, Japan and Canada. 1.7  CONCLUSIONS Developing the means to understand the economic values associated with ocean and coastal resources is an essential part of efforts to restore, maintain, and enhance the oceans as a sustainable source of wealth (Colgan, 2016). However, due to the difficulty in defining and conceptualizing the industrial sectors that utilize the seas and oceans within their value chain, the value of the marine sector has been previously under researched. However, as noted

 

Chapter One

14

Table 1.4  Largest Subsectors within the Marine Economy Country

Largest contributor

Second largest contributor

Ireland Australia UK USA France New Zealand China South Korea Japan Canada

Maritime Transport Coastal Tourism Oil and Gas Coastal Tourism Coastal Tourism Maritime Transport Maritime Transport Ship building Maritime Transport Oil and Gas

Coastal Tourism Oil and Gas Coastal Tourism Maritime Transport Ship building Fishing Coastal Tourism Maritime Transport Fishing Fishing

by Kildow and McIlgorm (2010) estimates of the magnitude and importance of coastal industries are critical in tracking economic health in coastal nations. They become especially relevant to the emerging climate change and inundation issues facing coasts internationally. Economic measures are important to forecast implications from these impacts, as are economic measures, indicating the vulnerability and resilience of different areas of the ocean economy. Thus, a coherent set of indicators detailing the economic impact of the marine sector is required. While the definitional scope of the marine sector has varied across countries the most important outcome of the research outlined in this chapter has been the conceptual development of the marine sector as (a) a holistic entity and (b) recognizable in the twenty-​first century. Academics and policy makers interested in the marine resource now have a set of tools to measure and analysis he extent of the marine economy in their country of interest. These tools may now be used to better inform future marine planning and investment decisions at both the national and international levels. A second important point to note is that while the marine sector is traditionally seen as a sector dominated by the seafood industry, the majority of international studies highlighted in table 1.3 found that it was the marine service sector, particularly maritime transport and marine tourism that provided the largest contribution of GDP, GVA, and employment to the marine economy. BIBLIOGRAPHY Allen Consulting (2004). The economic contribution of Australia’s marine industries: 1995–​96 to 2002–​03. A Report prepared for the National Oceans Advisory Group. The Allen Consulting Group Pty Ltd., Australia.

 



Introducing the Marine Economy

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Ban, N. C., Bodtker, K. M., Nicolson, D., Robb, C. K., Royle, K., Short, C. (2013). Setting the stage for marine spatial planning: Ecological and social data collation and analyses in Canada’s Pacific waters. Marine Policy 39: 11–​20. Böhnke-​Henrich A., Baulcomb C., Koss R., Hussain S., de Groot S. (2013). Typology and indicators of ecosystem services for marine spatial planning and management. Journal of Environmental Management 130: 135–​145. Cicin-​ Sain, B., Knecht, R.t W. (1998). Integrated Coastal and Ocean Management: Concepts and Practices. Island Press, Washington, DC. Chang Jeong-​In. (2015). A Preliminary Assessment of the Blue Economy in South Korea. Powerpoint presented in the Inception Workshop on Blue Economy Assessment, Manila, July 28–​30, 2015. Colgan C. S. (1997). Estimating the value of the ocean in a national income accounting framework, preliminary estimates of gross product originating for 1997. National Ocean Economics Project, Working Paper 1. Colgan, C. S. (2003). Measurement of the Ocean and Coastal Economy: Theory and Methods. Publications. Paper 3, http://​cbe.miis.edu/​noep_​publications/​3 Colgan, C. S. (2016). Measurement of the ocean economy from national income accounts to the sustainable blue economy. Journal of Ocean and Coastal Economics 2(2), Article 12. DOI: http://​dx.doi.org/​10.15351/​2373-​8456.1061 Colgan, C. S. and J. Plumstead (1993). Economic Prospects for the Gulf of Maine. Augusta, ME, Gulf of Maine Council on the Marine Environment. Colgan C. (2013). The ocean economy of the United States: Measurement, distribution and trends. Ocean and Coastal Management 71: 334–​343. De Maio, A., Irwin, C. (2016). From the orderly world of frameworks to the messy world of data: Canada’s experience measuring the economic contribution of maritime industries. Journal of Ocean and Coastal Economics 2, Article 9. DOI: http://​ dx.doi.org/​10.15351/​2373-​8456.1049 Douvere, F., Ehler, C. N. (2009). New perspectives on sea use management: initial findings from European experience with marine spatial planning. Journal of Environmental Management 90(1): 77–​88. ESRI. (2004). A National Survey of Water-​Based Leisure Activities in Ireland 2003. A Report presented to the Marine Institute, Economic and Social Research Institute, Dublin. Flannery, W., O’Cinneide, M. (2008). Marine spatial planning from the perspective of a small seaside community in Ireland. Marine Policy 32: 980–​987. Flannery, W., Ellis, G., Ellis, G., Flannery, W., Nursey-​Bray, M., van Tatenhove, J.P., Kelly, C., Coffen-​Smout, S., Fairgrieve, R., Knol, M. and Jentoft, S., 2016. Exploring the winners and losers of marine environmental governance/​Marine spatial planning: Cui bono?/​“More than fishy business”: epistemology, integration and conflict in marine spatial planning/​Marine spatial planning: power and scaping/​Surely not all planning is evil?/​Marine spatial planning: a Canadian perspective/​ Maritime spatial planning–​ “ad utilitatem omnium”/​ Marine spatial planning:“it is better to be on the train than being hit by it”/​Reflections from the perspective of recreational anglers .... Planning Theory & Practice 17(1):  121–​151.

 

16

Chapter One

Ellis, G., Flannery, W. (2016). Marine spatial planning: Cui bono? Planning Theory & Practice 17(1): 121–​151. DOI: 10.1080/​14649357.2015.1131482. Girard, S., Kalaydjian, R. (2014). French Marine Economic Data 2013, 102 pp.Ifremer, Brest, France. DOI: 10.13155/​36455. Hynes, S., Farrelly, N. (2012). Defining standard statistical coastal regions for Ireland. Marine Policy 36(2): 393–​404. Kildow, J. T., McIlgorm, A. (2010). The importance of estimating the contribution of the oceans to national economies. Marine Policy 34: 367–​374. Kalaydjian, R. (2008). French Marine-​ related Economic data, 2007. Marine Economics Department, IFREMER, Brest, France. Kalaydjian, R. (2016). Maritime accounts in the European Union: Coping with limited information. Journal of Ocean and Coastal Economics 2, Article 2. DOI: http://​ dx.doi.org/​10.15351/​2373-​8456.1050 Kalaydjian, R., Daurès, F., Girard, S., Van Iseghem, S., Levrel, H., & Mongruel, R. (2010). French marine economic data 2009. IFREMER, France. Kildow, J., Powell, H. K., Colgan, C., Bruce, E. (2000). The National Ocean Economics Project: The Contribution of the Coast and Coastal Ocean to the U.S. Economy, Research and Strategy Plan. National Ocean Economics Project, USC, Wrigley Institute. Kildow, J. T., Colgan, C. S., Scorse, J. (2009). State of the U.S. Ocean and Coastal Economies 2009. National Ocean Economics Program. Luger, M. (1991). The economic value of the coastal zone. Environmental Systems 21(4):  278–​301. Mandale, M., Foster, M., Chiasson, P. Y. (2000). The Economic Value of Marine Related Resources in New Brunswick. New Brunswick Department of Fisheries and Aquaculture, Fredericton NB. Mandale, M., Foster, M., Plumstead, J. (1998). Estimating the Economic Value of Coastal and Ocean Resources: The Case of Nova Scotia. Oceans Institute of Canada, Chester NS. McIlgorm, A. (2016). Ocean Economy Valuation Studies in the Asia-​ Pacific Region: Lessons for the Future International Use of National Accounts in the Blue Economy. Journal of Ocean and Coastal Economics, 2(2): 6. Morrissey K. (2015). An inter and intra-​regional exploration of the marine sector employment and deprivation in England. The Geographical Journal 181(3), 295–​303. Moeller, R. M., Fitz, J. (1994). Economic Assessment of Ocean Dependent Activities. California Research Bureau, Sacramento, CA. Morrissey, K., O’Donoghue, C., Hynes, S. (2011). Quantifying the value of multi-​ sectoral marine commercial activity in Ireland. Marine Policy 35(5): 721–​727. Nakahara, H. (2009). Economic contribution of the marine sector to the Japanese Economy. Research Institute for Ocean Economics. In A. Ross Ed. “The Marine Economy in Times of Change,” PEMSEA, Tropical Coasts Vol. 16 No. 1 pp. 49–​53. Nathan Associates (1974). Gross Product Originating from Ocean-​Related Activities. Bureau of Economic Analysis, Washington, DC.

 



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Nuryadin, D., Syaifudin, N., Handika, R., Setyobudi, R. H., Udjianto, D. W. (2016). The Economic of Marine Sector in Indonesia. Aquatic Procedia 7: 181–​186. NZ Stats. (2006). New Zealand’s Marine Economy 1997 to 2002. An experimental series report by statistics. New Zealand. OECD (2016). The Ocean Economy in 2030. OECD Publishing, Paris. O’Mahony, C., Gault, J., Cummins, V., Kopke, K., O’Suilleabhain, D. (2009). Assessment of recreation activity and its application to integrated management and spatial planning for Cork Harbour, Ireland. Marine Policy 33: 930–​937. Park, K. S., Kildow, J. T. (2014). Rebuilding the classification system of the Ocean economy. Journal of Ocean and Coastal Economics, 2014(1): 4. Peng, B., Hong, H., Xue, X., Jin, D. (2006). On the measurement of socioeconomic benefits of integrated coastal management (ICM): Application to Xiamen, China. Ocean & Coastal Management 49: 93–​109. Pontecorvo, G. (1988). Contribution of the ocean sector to the U.S. economy: Estimated values for 1987—​A technical note. Marine Technology Society Journal 23(2): 7–​14. Pontecorvo, G., Wilkinson, M., Anderson, R., Holdowsky, M. (1980). Contribution of the ocean sector to the U.S. economy. Science 208 (30 May 1980): 1000–​1006. Pugh, D. (2008). Socio-​economic indicators of marine-​related activities in the UK economy. The Crown Estate, London. RASCL. (2003). Canada’s ocean industries: contribution to the economy 1988–​2000. Prepared for the Economic and Policy Analysis Division, Department of Fisheries and Oceans by Roger A. Stacey Consultants Ltd. (RASCL), Ottawa, Ontario K1V 1K8; September 2003. Stojanovic, T. A., & Farmer C. (2013). The development of world oceans & coasts and concepts of sustainability. Marine Policy 42: 157–​165. Talento, R. J. (2016) Accounting for the Ocean Economy Using the System of National Accounts. Journal of Ocean and Coastal Economics 2(2), Article 5. DOI: http://​dx.doi.org/​10.15351/​2373-​8456.1048 Zhao, R., Hynes, S., Shun He. G. (2013). Defining and quantifying China’s ocean economy. Marine Policy 43: 163–​173. Zhao, R., Hynes, S., & He, G. S. (2014). Defining and quantifying China's ocean economy. Marine Policy 43: 164–​173.

 

 

Chapter Two

The Marine Economy A National Perspective

2.1  INTRODUCTION As outlined in Chapter 1, an increased understanding of the importance of market (commercial activity) and nonmarket (climate regulation and food production) value of the marine resource has led to the realization that empirical evidence at both a national and subnational level is required to inform public policy, governance and regulation across the sector (Morrissey et al., 2011; Colgan, 2013). Within this context, there has been an increasing interest in the use of economic instruments in the evaluation of the marine resource. Chapter 1 outlined international research that has undertaken an economic valuation of the marine economy. Based on Colgan (2003) concept of ocean GDP, these studies found that the marine economy provided between 0.7 percent of GDP (country) and 9.6 percent of GDP (country). An increasing number of international studies have therefore addressed the direct economic value of the marine resource. However, sectors do not exist in a vacuum; rather they rely on other sectors for inputs into their production process, while simultaneously selling their output to other sectors to generate profit. Thus, these national (or regional) level analyses do not fully reflect the significant role that the marine sector plays in the national economy, as they do not take into consideration the various ways economic activity in the sector affects other branches of the economy. Examining just one subsector of the marine sector, the fisheries sector, Sigfusson et al. (2013) provide an overview of the industries that the fisheries sector sell to, and purchase from (­figure  2.1). From ­figure  2.1, it is clear that the fisheries sector buys and sells goods and services from a range of sectors, in the marine economy more broadly (e.g., shipping and harbor operations) and outside the marine sector (e.g., chemical industries, rubber 19

 

20

Chapter Two

Figure 2.1.  Connections between Industries in the Icelandic Fisheries Cluster (Sigfusson et al., 2013).

and plastic manufacturing). Activities in the marine sector not only directly affect the industries in the marine sector, but also influence other sectors through their purchases and sales. These purchases and sales are referred to as intersectoral linkages. Considering the complexity of the intersectoral linkages depicted for the Icelandic fisheries sector in ­figure  2.1, it would be an enormous task to trace and measure all the purchases and sales of all the subsectors within the marine economy. Fortunately, in the 1930s economist Wassily Leontief devised a simple procedure to trace the purchasing and sales behavior of sectors using the IO model. Since its development, IO models have become an important tool for regional scientists (ref). However, while the computation of linkages is straightforward within an IO framework and these tables are produced to document the national accounts of most countries, the data limitations surrounding marine-​based sectors outlined in this chapter have meant that there has been limited use of IO analysis within the sector. A notable exception is an early paper by Andrews and Rossi (1986) that provided the economic impact of commercial fisheries and marine activities in the northeast of the United States. More recent literature include analyses of the Hawaiian (Cai et al., 2005; Coffman & Kim, 2010) and Australian (Norman-​López & Pascoe, 2011)

 



The Marine Economy

21

fisheries sector, analysis of the German (Van Der Linden, 2001), Taiwanese (Chiu & Lin, 2012) and Irish (Morrissey & O’Donoghue, 2013) maritime transportation sector, an analysis of Welsh ports (Bryan et al., 2006) and analysis of the South Korean (Kwak et al., 2005) Irish (Morrissey & O’Donoghue, 2012) and the Indonesian (Nuryadin et al., 2016) marine sector. Section 2.2 outlines the concept of intersectoral linkages. Section 2.2 outlines the linkage theory within the context of national economies. Section 2.3 outlines the mathematical theory of IO modeling and how applicable it is to marine sector modeling. Using Morrissey and O’Donoghue (2012) paper as a case study, section 2.4 demonstrates how an IO table can be used to trace the purchases and sales of the marine sector within the Irish economy. Section 2.5 offers concluding comments on how the findings from an IO table may be used for direction national and international policy and planning in the marine sector. 2.2  LINKAGES In an interdependent economy, a sector is linked to other sectors by its direct and indirect purchases and sales. Conceptualizing these connections in an IO framework, production by a particular sector has two kinds of the economic effects on other sectors in the economy. These are known as forward and backward linkages. The backward-​linkage effect is the direct and indirect effect on the production of all the industries, which provide the intermediate inputs necessary for the production of a particular industry being explored (Kim et al., 2002). The backward linkage is a measure that is expressed in terms of a sector’s use of inputs from other sectors in the economy. Conceptually, if a sector, sector j, increases its output, this means there will be increased demands from sector j (as a purchaser) on the sectors whose products are used as the inputs to production in j. The term backward linkage is used to indicate the interconnection of a particular sector to those sectors from which it purchases inputs (Miller & Blair, 1985). As will be presented mathematically in Section 2.4 of this chapter, a sector’s backward-​linkage index is calculated by dividing its Leontief supply-​driven multiplier by the average Leontief supply-​ driven multipliers for all the sectors (Dietzenbacher, 2002). The larger this value for a sector, the greater the sector dependence on others in the economy for its inputs, and therefore the more the economy might be expected to be stimulated by an increase in this sector’s output (Aroca, 2001). In other words, a dollar’s worth of expansion of this sector output would be more beneficial to the economy than would an equal expansion of other sectors’ output with lower values, in terms of the productive activity throughout the economy that would be generated by it (Miller & Blair, 1985).

 

22

Chapter Two

As a rule of thumb, a backward-​linkage index higher than one implies that the sector has strong backward linkage relative to other sectors in the economy. Symmetrically, the forward-​linkage effect is the direct and indirect effect on the production of all other industries, which use the output of the specific industry being explored for the production of their own intermediate goods and services. The forward linkage indicates the proportion of sector output that serves as inputs to all sectors in the regional economy. The larger a sectoral forward linkage, the more its output is used as an input to production in the regional economy. Conceptually, increased output in sector j also means additional amounts of product j that are available to be used as inputs to other sectors for their own production. That is, there will be increased supplies from sector j (as a seller) for the sectors, which use goods from sector j in their production. The term forward linkage is used to indicate this kind of interconnection of a particular sector to those sectors to which it sells its output (Miller & Blair, 1985). Forward linkage indices can be calculated similarly by using the Ghosh supply-​driven multipliers. The backward linkage is expressed as the power of dispersion while the forward linkage is expressed as the sensitivity of dispersion. If some industries have both power of dispersion and sensitivity of dispersion values greater than 1 for both forward and backward linkages, these industries play important roles in economic development in supporting other industries as well as boosting other industries. On the other hand, if the values of these measures are smaller than 1, the industries are not only less stimulated by overall industry growth than other sectors, but also have smaller effects in terms of investment expenditures on the national economy (Kwak et al., 2005; Han et al., 2004). Backward linkage effects are strongly induced by industries with high intermediate input coefficients, such as manufacturing industries (Kwak et al., 2005). Symmetrically, strong forward linkages are generally induced by the primary and material industries, whose outputs are used by other industries as intermediate goods (Kwak et al., 2005). The intensity of intersectoral linkages between related industry groups has been highlighted as a key determinant of the technical and competitive progress of an economy (Rasmussen, 1956). As such, the identification of sectors that display strong linkages is believed to be a useful planning tool for stimulating economic growth at the sectoral, regional and national level (Hirschman, 1958). The next section provides a mathematical overview of the IO framework. 2.3  INPUT–​OUTPUT ANALYSIS IO analysis is a top-​down macroeconomic technique that usually uses sectoral monetary transactions data to account for the complex interdependencies of

 



The Marine Economy

23

industries in an economy (Lenzen, 2003). The standard approach to assessing inter-​industry linkages begin with a conventional representation of IO relations in an economy (Kwak et al., 2005): x = Xe- + f ​



⇒ x = A x + f (2.1)



⇒ x = (I –​A)-​1 f

where matrix X represents the transaction flows between sectors of activities and is the sum of gross outputs, matrix I is an identity matrix, vector x is the sum of gross outputs, vector f represents the part of gross output sold to final demand, and A is a matrix of input coefficients defined as; A = aij =�



zij

(2.2)

xj

where zij is intermediate demand for inputs between sector i and the supply sector j and xj is the final output for sector i. (I – ​A)–​1 (eq. 2.1) is known as Leontief’s inverse matrix and represents the total direct and indirect outputs in sector i per unit of exogenous final demand, d for sector j. However, this standard demand model cannot exactly assess the effects of new production activity in an industry on all other sectors of the economy because changes in the final demand come about as a result of forces outside the model, for example, changes in consumer tastes or increased government purchases. For this purpose, the individual maritime sector needs to be handled as exogenous and put into the final demand group (Kwak et al., 2005; Cai & Leung, 2004). Decomposing final demand into marine based final demand fi and non-​marine final demand fj, and outputs xj and xi and direct input coefficient matrix respectively, one can derive a variant of the Leontief IO model as follows:



 xi   Aii  x  =  A j ji

Aij   xi   fi  + Ajj   x j   f j 

(2.3)

where i denotes the marine sector and j denotes the rest of the economy. Thus

(

x j = I − Ajj

)

−1

(

⋅ Aji xi + I − Ajj

)

−1

f j

(2.4)

 

Chapter Two

24

The additional output for sectors j generated by final demand for those −1 sectors is I − Ajj f . The contribution made by the marine sector to other sectors ∆x j is;

(



)

(

∆x ′j = x j − I − Ajj

)

−1

(

f j = I − Ajj

)

−1

⋅ Aji xi

(2.5)

Based on this partitioned IO model, the backward linkage from one unit of output change in the marine sector i can be calculated by;

(

∆x j = I − Ajj

)

−1

⋅ Aji

(2.6)

Summing these elements and the initial unit output change in the marine sector i would give a measure of the sectors backward linkage impacts (eqs. 2.6, 2.7). Thus, the marine sectors, i Leontief supply driven (LSDi) multiplier is given by;

(

LSDi = 1 + e ′ I − Ajj

)

−1

⋅ Aji

(2.7)

where 1 represents the initial unit output change in the marine sector i, and e is the summation vector used to aggregate the elements in Δxj, that is, the impacts of this initial output change on the rest of the economy through the marine sectors i’s backward linkages. To facilitate linkage comparison among the industries, one may calculate a backward linkage index by using the following formula (Midmore et al., 2006): BLI i =



LSDi

 ∑ LSDk   k   K   

(2.8)

where K is the number of industries within the IO table. Capturing the intersectoral backward linkages of the marine sector, eq. (2.8) can be used to evaluate the impact of a change in marine activity across the ten marine sectors on the output of all other sectors, that is, the production-​inducing effect (Kwak et al., 2005). In terms of using this information as a policy tool, the backward linkage effect, is the power of dispersion, calculated as the average of n elements in column j of the Leontief inverse matrix divided by the average of all n2

 



The Marine Economy

25

elements (Cai & Lueng, 2004). A backward-​linkage index higher than one implies that the sector has strong backward linkage relative to other sectors in the economy. An industry with high backward linkages than other industries indicates that the expansion of its production is more beneficial to the economy in terms of inducing productive activities. With regard to forward linkages, the use of Leontief row sums is controversial as they calculate measures of forward linkages based on the strength of backward linkages (Cai & Lueng, 2004; Cai et al., 2005). As such, the forward oriented Ghoshian model, although criticized itself (Cai et al., 2006), is a popular alternative (Cai & Lueng, 2004). In contrast to the Leontief model which relates multiple inputs to each output, the Ghoshian model relates multiple outputs to each input. As such the Ghoshian model uses fixed exogenous intermediate coefficients, β, derived from the rows of the IO table such that;



( x′ j

 Bii x ′j = xi′ x ′j   B ji

) (

)

Bij  + wi′ w′j B jj 

(

)

(2.9)

where i represents the marine sector and j all other sectors and x denotes output. B is the direct output coefficient matrix and w represents primary inputs. Utilizing a similar derivation as with the backward linkage, one can define the impact of a one unit output change in the marine sector on the output of other sectors as:

(

∆x j = Bij I − B jj

)

−1

(2.10)



Summing and expressing as a ratio of all other forward linkages, one can produce the Ghoshian supply driven (GSD) multiplier:

(

GSDi = 1 + Bij I − B jj

)

−1

e

(2.11)

Capturing the intersectoral forward linkages of the marine sector, eq. (2.11) can be used to evaluate the impact of a price or supply change across the ten marine sectors on the output of all other sectors, that is, the supply-​inducing effect. The forward linkage effect is expressed as the sensitivity of dispersion, which is the average of n elements in row i of the Leontief inverse matrix divided by all n2 elements (Cai & Lueng, 2004). A forward-​linkage index higher than one implies that the sector has strong forward linkage relative to other sectors in the economy (Cai & Lueng, 2004). In terms of policy application, an industry with higher forward linkages than other industries means

 

26

Chapter Two

that its production is relatively more sensitive to changes in other industries output. Backwards and forward linkages provide a quantitative measure of the relationship among industries that can be organized into a rank-​sized hierarchy. This therefore provides policymakers and practitioners with a quantitative measure of the each industry’s structural relationship within the wider economy. Thus, allowing policy decisions on investment to be based on the relative importance of a sector within an economy. 2.4  CASE STUDY: THE IRISH MARINE ECONOMY, AN INPUT–​OUTPUT ANALYSIS This section outlines the disaggregation of the national Irish IO table to contain ten additional marine sectors. These sectors included fishing and aquaculture, seafood processing, oil and gas extraction and production, marine engineering, marine water construction, boat building, maritime transportation, auxiliary services to maritime transport, marine water-​based tourism activities, and marine retail. Full details of the disaggregation can be found in Morrissey and O’Donoghue (2013). Using Morrissey and O’Donoghue (2013) disaggregation of a national IO table to include ten additional marine sub-​sectors demonstrates the deeper understanding of the economic value of the marine resource provided by an IO framework. As will be presented below, the IO analysis found that a number of marine sectors, notably the maritime transportation sector, have an important economic role within the wider Irish economy. Case Study: Data Requirements Disaggregating a national IO table to encompass a new sector requires detailed sectoral data on intermediate consumption (input coefficients), output, compensation of employees and final demand. Given the fragmented nature of the marine sector, a variety of the data types must be employed to collate the necessary data to disaggregate the national IO. Referring to ­chapter  2, these data types may be broken down into three broad categories. Type 1 data is data that is in the public domain. Such estimates are generally confined to those sectors whose connection to the sea is clear (i.e., commercial fisheries, coastal transportation). Type 2 data is data that is publicly collected but is not released into the public domain. This data is at a lower industrial or geographical classification and is therefore considered confidential. Type 3 data is data that is not available in the public domain. The sectors where there is no publicly available data are those for which it is difficult to recognize as marine based (Morrissey et al., 2011). For example, one cannot

 



The Marine Economy

27

differentiate between water-​based recreational activities and land-​based recreational activities. As such, to disaggregate the national IO table to include a marine component, public data is usually not sufficient to estimate the full value of the marine sector. In terms of collating nonpublic data (Type 2 data), the Irish Central Statistics Office (CSO) provides data on turnover, intermediate consumption, gross value added, exports, and employment for each sector within the Irish economy. This data is collected across a number of censuses and surveys. The censuses and surveys used for the collation of the data on the marine sector include; the Census of Industrial Production (CIP), the Annual Services Inquiry (ASI) and the Census of Buildings and Construction (CBC). These three micro datasets provide detailed firm and enterprise level information on the economic activities for each company at the four digit NACE code. In order to assure consistency of treatment across different datasets, the industry estimates should operate within an established measurement of economic activity, such as the national income and production accounts (Colgan, 2007). The CIP, ASI, and CBC data sets collected by the CSO form the basis for the calculation of Ireland’s national income and production accounts. Access may be granted to researchers interested in examining the data, through the CSO officer of statistics facility. With regard to marine-​based sectors where no data was available (Type 3 data) a survey was administrated to each company within each sector (Morrissey et al., 2011). The survey was prepared in line with the CSO surveys used to obtain data for the CIP, ASI, and CBC datasets. This ensured that the necessary data to disaggregate the national IO table; intermediate consumption, output, final demand and compensation of employees, compiled between public and non-​public data was consistent. Companies that provided both land-​based and marine-​based goods and services were specifically asked about their commercial marine-​based activity (i.e., what percentage of their turnover was derived from marine-​based activity). The central year for the study was 2007. To ensure temporal consistency, public datasets that were from earlier or later years were not included in the estimates. Data collected via survey specifically asked for company accounts for the year ending the 31st of December, 2007. Case study: Linkages Within the Marine Sector Table 2.1 presents the backward linkages for each sector within the Irish economy. A broad examination of the linkages within the Irish economy indicates that ‘mining and quarrying’ has the highest backward linkage score (134), followed by “seafood processing” (126), “water transport services” (109), “research and development services” (109) and “sewage and refuse

 

28

Chapter Two

Table 2.1  Backward Linkages within the Irish Economy 2007 Sector

Backward linkages

Other mining and quarrying

134

Seafood Processing Water transport services Research and development services Sewage and refuse disposal services Water Construction Post and telecommunication services Forestry Construction work

126 109 109 107

Sector

Backward linkages

Public administration and defence Leather and leather products Wholesale trade Air transport services

52

49

51 51 51

Membership organization services Water collection and distribution Electricity and gas Wood and wood products (excl furniture) Fishing Basic metals Agriculture

90

Pulp, paper and paper products Rubber and plastics Medical, precision and optical instruments Other services Motor fuel and vehicle trade and repair Oil & Gas Extraction

87

Auxiliary Transport Marine

44

81 80

Textiles Machinery and equipment

39 37

77 77 76

37 36 36

Other non-​metallic mineral products Boat Building

76

Motor vehicles and trailers Real estate services Renting services of machinery and equipment Fabricated metal products

29

Auxiliary transport services and travel agencies Services auxiliary to financial intermediation Electrical machinery and apparatus Marine Engineering

73

Extraction of coal, peat, petroleum and metal ores Petroleum and other manufacturing products Financial intermediation services Education

27

Hotel and restaurant services

68

Land transport services

65

Marine Retail WBA Computer and related services

63 62 61

Insurance and pension services Radio, television and communications apparatus Health and social work services Other business services Other transport equipment Printed matter and recorded media

106 103 96 94

73

72 71 69

47 47 47 46 44

32

29 29 29

26 26 24 23 16 (Continued )

 



The Marine Economy

29

Table 2.1  (Continued) Sector

Backward linkages

Recycling Recreation

58 57

Food and beverages

56

Retail trade and repair of household goods

55

Sector Wearing apparel Office machinery and computers Chemical products and man-​ made fibres Private Households

Backward linkages 13 13 12 0

disposal services” (107). However, what is of interest is that within the wider Irish economy, three marine sectors are ranked within the top ten sectors with the strongest backward linkages—​seafood processing (126), maritime transportation (109), and water construction (106). Each of these sectors has a backward linkage greater than one, implying that these sectors are important input suppliers to other sectors. Examining the magnitude of the linkages for these three sectors in more detail, water construction is 1.06. This implies that for every €1 produced within the water construction sector, €0.06 is backward linked to its direct and indirect upstream suppliers. The magnitude of the water transportation sectors backward linkage is €1.09. This implies that for every €1 produced within the water transportation sector, €0.09 is backward linked to its direct and indirect upstream suppliers. Four cents of this €0.09 belong to the water transportation sectors, direct suppliers and €0.05 belongs to its indirect suppliers (e.g., the suppliers of its direct suppliers). Seafood processing has the strongest backward linkage, €1.26, within the marine sector. This implies that for every €1 produced within the seafood processing sector, €0.26 is backward linked to the sectors direct and indirect upstream suppliers. Two cents of this €0.26 belongs to the seafood processing sectors direct suppliers and €0.24 belongs to its indirect suppliers (e.g., the suppliers of its direct suppliers). Overall, the average backward linkage for the Irish economy was 58. This indicates that the sectors in the wider Irish economy had low (less than one) backward linkage effects. Ireland is a small open economy and many of its inputs into the process of production are imported from outside the country. Indeed, further analysis of the Irish IO table found that on average imports for each of the sixty-​two sectors as a percentage of inputs was 60 percent. In contrast, within the marine sector, the ratio of imports to exports in three sectors, seafood processing, water construction and water transportation sectors are 0.06 percent, 15 percent, and 16 percent, respectively.

 

30

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It was further found that the high backward linkage within the seafood processing sector is due to the strong links between the sector and the fishing sector (41), the seafood processing sector itself (9) and wholesale trade (8). The high backward linkage within the water transportation sector is due to the strong links between the indigenous water transportation sector itself (47), auxiliary marine transport service sector (e.g., liner and port services, 18), and computer services (8). The high backward linkage within the water construction sector is due to the strong links between the wider construction sector (46), wholesale trade (8), and other nonmetallic mineral products (8). This indicates the key linkages between these three marine sectors and indigenous companies within the Irish economy. Case Study: Forward linkages Table 2.2 presents the sectors that are most strongly forward linked within the Irish economy. A broad examination of the linkages within the Irish economy indicates that “forestry” has the highest backward linkage score (199), followed by “other mining and quarrying” (185), “recycling” (176), “other nonmetallic mineral products” (148), and “post and communications” (139). Overall, the average forward linkage for the Irish economy was 62. From table 2.2, one can see that the only one marine sector, maritime transportation, Table 2.2  Forward Linkages in the Irish Economy and the Irish Marine Sector Sector

Forward linkages

Sector

Forward linkages

Forestry Other mining and quarrying Recycling Other non-​metallic mineral products Post and telecommunication services Electricity and gas

199 185 176 148

Construction work Water Construction Recreation WBA

54 53 52 52

139

46

Water transport services

120

Wood and wood products (excl furniture) Services auxiliary to financial intermediation Fabricated metal products Sewage and refuse disposal services Agriculture

116

111 110

Petroleum and other manufacturing products Renting services of machinery and equipment Membership organization services Medical, precision and optical instruments Insurance and pension services Real estate services Textiles

109

Seafood Processing

137

114

45 41 39 34 32 30 28 (Continued )

 



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31

Table 2.2  (Continued) Sector

Forward linkages

Sector

Forward linkages

Auxiliary transport services and travel agencies Land transport services Auxiliary Transport Marine Other business services

103

Hotel and restaurant services

25

99 95 95

24 20 14

Marine Engineering Pulp, paper and paper products

95 93

Research and development services Computer and related services

93

Financial intermediation services Motor fuel and vehicle trade and repair Rubber and plastics Water collection and distribution Air transport services Fishing Other services Extraction of coal, peat, petroleum and metal ores Oil & Gas Extraction

78

Food and beverages Leather and leather products Radio, television and communications apparatus Education Health and social work services Public administration and defence Printed matter and recorded media Chemical products and man-​ made fibres Basic metals

Wholesale trade Electrical machinery and apparatus

60 59

84

74 72 72 72 69 64 62 62

12 12 11 9 8 8

Motor vehicles and trailers Office machinery and computers Machinery and equipment Other transport equipment Boat Building Wearing apparel

6 4

Retail trade and repair of household goods Marine Retail Private Household

0

1 1 1 0

0 0

has a forward linkage greater than one (120). This implies that every €1 produced by the maritime transportation sector is forward linked to €0.20 to the production of the sectors direct and indirect downstream demanders. In detail, for €1 of the production of water transportation services, €0.49 is sold directly for final consumption, including €0.08 for local consumption and €0.41 for exports. The rest €0.20, are bought by the water transportation sectors downstream demanders. Within the wider Irish economy the water transportation sector has the seventh highest forward linkage. Placing the high forward linkage demonstrated by the water transportation sector in context to the other nine marine sectors, Ireland is a small, open economy and its island status means that

 

32

Chapter Two

sectors in the wider economy rely on water transportation as a means of importing and exporting goods. Thus, given the structure and geo-​economic status of the country, maritime water transportation is an important intermediate service in the production process of Irish industrial and manufacturing sectors. This is particularly true of the wholesale trade and post and communications sectors, which display forward linkages of 15 and 10 to the water transportation sector, respectively. The small forward linkages of the other nine marine based sectors, particularly the marine retail, boat building, seafood processing, water based activities and water construction reflects the fact that for these sectors almost all of their goods and services are sold for final consumption. Production-​Inducing Effects of the Irish Marine Sector Further to allowing an analysis of intersectoral linkages within an economy, IO analysis also makes it possible to quantify all the repercussions generated by an increase in demand in a sector or group of sectors, which might not, at first sight, seem connected with it (Perez-​Labajo, 2001). The impacts of a €1 change in marine investment on the wider Irish economy for the ten marine sectors are presented in table 2.3. From table 2.3 one can see that the total production inducing effect for the marine sector is €6.31. The marine sector has the largest impacts on the construction, other business services and financial intermediate service sector. To place these results within the context of the wider Irish economy, table 2.4 presents the five sectors with the highest turn­ over/​output in 2007. Comparing ­tables 2.3 and 2.4, one can see that increased investment in the marine sector would have the strongest production inducing impact on three of the five sectors with the highest turnover in 2007 (financial intermediation services, wholesale trade and construction work). This would therefore indicate that stimulating investment in the marine sector would positively affect sectors that have the greatest impact on the Irish economy. To continue the analysis, table 2.5 provides a breakdown of the production multipliers by (a) individual marine sector and (b) the sectors it has the greatest impacts on within the wider economy. From table 2.5, one can see that an €1 investment in water construction has the largest impact on the Irish economy and generates €1.01 additional spending in the economy. Marine auxiliary transport services have the lowest (€0.39). In terms of individual downstream sectoral impacts, examining table 2.5 as a whole, one can see that the sectors receiving the largest downstream impacts are financial intermediation services (marine engineering; oil and gas extraction, 0.1; processing 0.08), insurance and pension services (Fishing, 0.7; Boat Building, 0.14) and other business services (WBA, 0.2; Marine retail, 0.1). Thus, while the marine sector is often seen as a “traditional” primary production-​oriented

 



The Marine Economy

33

Table 2.3  Production-​Inducing Effect of the Irish Marine Sector Sector

Multiplier

Construction work

0.700

Other business services Financial intermediation services Insurance and pension services

0.676 0.631 0.397

Wholesale trade

0.372

Computer and related services

0.355

Auxiliary transport services and travel agencies Electricity and gas

0.354

Real estate services

0.270

Post and telecommunication services Land transport services Hotel and restaurant services Services auxiliary to financial intermediation Renting services of machinery and equipment Other non-​metallic mineral products Petroleum and other manufacturing products Air transport services Extraction of coal, peat, petroleum and metal ores

0.265

Motor fuel and vehicle trade and repair Fabricated metal products Sewage and refuse disposal services Medical, precision and optical instruments Public administration and defence Other services

0.094

Food and beverages Recreation

0.051 0.047

Sector

Multiplier

Printed matter and recorded media Rubber and plastics Wood and wood products Membership organization services Other mining and quarrying Research and development services Chemical products and man-​made fibres Pulp, paper and paper products Electrical machinery and apparatus Health and social work services Agriculture Education Recycling

0.046

0.009

0.113

Water collection and distribution Forestry

0.113

Textiles

0.007

0.106 0.095

Other transport equipment Radio, television and communications apparatus Motor vehicles and trailers

0.005 0.005

Basic metals Office machinery and computers Retail trade and repair of household goods Machinery and equipment

0.001 0.000

Leather and leather products Wearing apparel Total Impact

0.000

0.289

0.189 0.176 0.134 0.133

0.079 0.071 0.070 0.063 0.058

0.041 0.040 0.038 0.035 0.032 0.028 0.024 0.023 0.023 0.023 0.016 0.010

0.008

0.001

0.000 0.000

0.000 6.31

 

Chapter Two

34

Table 2.4  Top Five Sectors (Turnover) in Ireland 2007 Sector

Turnover (€’000)

Food and beverages Financial intermediation services Wholesale trade Chemical products & man-​made fibers Construction work

19,176 20,218 20,855 33,592 47,587

Table 2.5  Marine Production Multipliers and the Downstream Sectors Greatest Impacted Marine Sector Total Impact (€) Fishing 0.57 Oil & Gas

0.43 Seafood Processing 0.74 Boat Building

0.73 Water Construction 1.06 Water Transport

0.58 Auxiliary Marine Transport Services 0.39 Marine Engineering 0.68

Sub-​Sectors receiving the greatest impact by individual marine sector (€) Insurance and pension services 0.07 Financial intermediation services 0.11 Financial intermediation services 0.08 Insurance and pension services 0.14 Construction work 0.46 Auxiliary transport services and travel agencies 0.18 Computer and related services

0.06 Financial intermediation services 0.14

Electricity and gas

Construction work

0.06 Electricity and gas

0.05 Other business services

0.05 Other business services

0.04 Wholesale trade

0.08 Financial intermediation services 0.08 Other non-​metallic mineral products 0.08 Computer and related services

0.08 Construction work

0.06 Wholesale trade

0.08 Other business services

0.08 Financial intermediation services 0.04 Auxiliary transport services

0.06 Other business services

0.06 Post & telecommunication

0.07

0.06 (Continued )

 



The Marine Economy

35

Table 2.5  (Continued) Marine Sector Total Impact (€)

Sub-​Sectors receiving the greatest impact by individual marine sector (€)

Marine Retail

Other business services

Real estate services

0.62 WBA

0.12 Other business services

0.51

0.15

0.10 Extraction of coal, peat, petroleum and metal ores 0.06

Financial intermediation services 0.05 Financial intermediation services 0.04

sector, these results indicate that the sector has strongest linkages with the service-​based sectors. Employment-​Inducing Effects of the Marine Sector Policymakers are frequently preoccupied with the employment-​ creating effects of industrial expansion. The marine sector is specifically believed to be of high employment benefit to local and coastal communities (O’Donnchadha et al., 2001; Collier, 2001; Vega et al., 2014), particularly within the marine resource sectors, fishing, aquaculture, and seafood processing. For this reason, it is important to be able to derive employment multipliers as well as production multipliers from the IO model. Table 2.6 presents the ranked employment inducing effects of all sectors in the Irish economy in 2007. From table 2.6, one can see that the agricultural (2.3), real estate (2), and construction (1.7) sectors had the highest employment inducing effect in 2007. Within the marine sector, the water construction sector (0.9) has the highest employment inducing effect and is the fifth highest sector across the whole Irish economy. This means that for every €100,000 invested in the water construction sector 0.9 individuals are employed (as full time equivalents, FTE). Taking the ten marine sectors together, the total impact of the marine sector on employment is 2.9 (water construction, 0.9; oil and gas extraction, 0.5; seafood processing 0.4; marine engineering 0.2; WBA, 0.2; boat building, 0.2; fishing 0.2; retail, 0.1; water transport services, 0.1; marine auxiliary transport services, 0.1). That is, for every €100,000 invested in the marine sector as a whole approximately three individuals, FTE, will be employed. To continue the analysis, table 2.6 provides a breakdown of these employment multipliers by (a) individual marine sector and (b) the sectors they have the greatest impacts on within the wider economy. In terms of water construction, the sector with the largest impact on employment, the sector generates

 

Chapter Two

36 Table 2.6  Employment Multipliers Sector

Multiplier

Agriculture

2.3

Real estate services

2.0

Construction work

1.7

Food and beverages

1.0

Water Construction Renting services of machinery and equipment Recycling Computer and related services Retail trade and repair of household goods Chemical products and man-​made fibres Research and development services Other business services

0.9 0.8

Hotel and restaurant services

0.6

Wholesale trade Office machinery and computers Other mining and quarrying Oil & Gas Extraction Printed matter and recorded media Seafood Processing Financial intermediation services Auxiliary transport services and travel agencies Basic metals Health and social work services Electrical machinery and apparatus Insurance and pension services Public administration and defence

0.4

Sector

Multiplier

Water collection and distribution Sewage and refuse disposal services Other non-​metallic mineral products Membership organization services Wood and wood products Education

0.3

Machinery and equipment Leather and leather products Radio, television and communications apparatus Services auxiliary to financial intermediation Marine Engineering

0.2 0.2 0.2

0.2

0.6 0.5

Petroleum and other manufacturing products Post and telecommunication services Water Based Activities Boat Building

0.5 0.5 0.5

Fishing Marine Retail Rubber and plastics

0.2 0.1 0.1

0.4

Pulp, paper and paper products Water transport services

0.1

0.1

0.4 0.4

Extraction of coal, peat, petroleum and metal ores Motor vehicles and trailers Auxiliary Transport Marine

0.4

Forestry

0.1

0.4

Fabricated metal products

0.1

0.3

Textiles

0.1

0.7 0.7 0.7 0.7 0.7 0.7

0.4

0.3 0.3 0.2 0.2 0.2

0.2 0.2

0.2 0.2 0.2

0.1

0.1 0.1

(Continued )

 



The Marine Economy

37

Table 2.6  (Continued) Sector

Multiplier

Electricity and gas

0.3

Land transport services Medical, precision and optical instruments Recreation Air transport services

Sector

Multiplier 0.1

0.3 0.3

Motor fuel and vehicle trade and repair Other services Other transport equipment

0.3 0.3

Wearing apparel Private Households

0.01 0.0

0.1 0.02

the largest downstream impacts to the construction sector (0.8 FTE), other business services (0.07 FTE), and wholesale trade (0.05 FTE). Examining the ten marine sectors individually the sectors receiving the largest downstream FTE employment impacts are other business services (Processing 0.1; Marine Engineering, 0.03; Water Transport, 0.06; Marine Retail, 0.07; WBA, 0.1; Marine Auxiliary Transport Services, 0.05) and construction (Fishing, 0.05; Boat Building, 0.1; water construction, 0.8). Similar to the analysis on marine production effects, the employment multiplier presented in this chapter indicates that the sector has the strongest impacts with the Irish service sectors. Case Study: Discussion In terms of identifying key sectors within an economy, the results of the linkage analysis indicate that three marine sectors, water transport, seafood processing and water construction, have backward linkages greater than 1. With regard to forward linkages, only one sector, water transport, has a forward linkage greater than 1. These results indicate that the Irish marine sector has more strength in absorbing products of related industries (higher backward linkages), rather than being used as an input by other industries (lower forward linkages). This implies that the marine industry has greater impacts in terms of investment expenditures on the national economy than other sectors as it has a relatively strong capacity for pulling or “spending” within other downstream industries. In terms of the production inducing effects presented in section 2.4, one can see that a marine-​based investment would have the largest impact on the service-​based industries, particularly the financial and insurance sectors. This indicates the proportionally higher costs of services sectors in the production costs of the marine sector (Kwak et al., 2005). However, as noted in section 2.4, increased investment in the marine sector would have the strongest impact on three of the five sectors with the highest turnover in 2007 (financial intermediation services, wholesale trade, and construction work). Thus, stimulating investment in the

 

38

Chapter Two

marine sector would positively affect sectors that have the greatest impact on the Irish economy. Within this context, the results presented within this paper indicate that the maritime transportation sector had the third highest backward linkage and seventh highest forward linkage in the Irish economy in 2007. In terms of backward linkages the analysis found that in 2007 the sector had high backward linkages with a number of professional-​and technology-​based services, such as the computer, insurance and banking sectors. Symmetrically, with regard to forward linkages the analysis showed that the maritime transportation sector was an important input into three of the most economically valuable sectors in the Irish economy—​the food and beverages sector, the construction sector and wholesale trade. In line with the cluster theory, the IO methodology presented above would indicate that given the strong linkages to a number of key service sectors already in place, and the large forward linkages to a number of key economic sectors, the future development of a maritime transportation cluster could potentially have large effects on the rest of the economy. With regard to employment, traditionally, marine employment was believed to center on the natural resource sector. However, the employment multipliers presented in section 2.4 demonstrate that employment in the sector is actually linked to more knowledge and technology intensive sectors in Ireland. Thus, these results would indicate that the promotion of policies that focus on knowledge-​based marine sectors, for example marine education and marine R&D, have the potential to have high employment impacts. In Ireland, similar to the experience noted in Malaysia (Saharuddin, 2001), a select few marine sectors including water transportation, oil and natural gas extraction, sea-​fishing and aquaculture have dominated policy in the Irish marine sector. However, as noted in the introduction, recent time has seen an increased realization of the technological potential embedded in the marine resource, particularly in the areas of biotechnology and functional foods and marine renewable energy. Within this context, the Sea Change Strategy 2007–​2013 (Sea change Strategy, 2007) encompassing both the National Marine Technology Program and the National Marine Biotechnology Program aims to create a sustained marine technology and marine biotechnology industry around relevant multidisciplinary, knowledge based sectors within Ireland. However, to estimate the impact of these programs, a coherent set of indicators detailing the economic impact on the Irish national and regional economy is required (Kildow & McIlgorm, 2010). This Case study has demonstrated the potential usefulness of the IO framework in examining key sectors within the marine sector and developing policies to ensure sustainable development of the marine sector within the wider Irish economy.

 



The Marine Economy

39

2.5  CONCLUSIONS As outlined in the Introduction, due to the difficulties in empirically defining (Kildow & McIlgorm, 2010; Colgan, 2013), data limitations and a lack of institutional concern with the marine sector (McIlgorm, 2016) there are few studies in which an IO model has been used for analyzing the marine sector. Given that a holistic description of a marine sector’s relationship with the wider economy is required to guide policy and investment, this chapter demonstrates the feasibility and value of extending a country’s IO table to include marine sectors. With regard to policy recommendations, comparing the strength of interindustry linkages as presented in section 2.4 can provide one mechanism for identifying strategic sectors for government investment. Production multipliers derived from the IO analysis may also be used to guide public investment decisions. The magnitude of production multipliers from the demand driven model can be interpreted as the direct and indirect benefits which would ensue from future marine based development projects. Using these multipliers, policy decisions on whether or not to conduct a proposed marine development project could be deduced by examining the magnitude of the marine sectors production inducing effect. To continue, IO analysis may also be used to provide empirical evidence for the development of spatial clusters (Midmore et al., 2006). Research in the early nineties (Porter, 1990) found that internationally competitive industries usually occur in the form of specialized clusters of “home-​based” industries, which are linked together through vertical relationships (buyers–​suppliers) or horizontal relationships (common customers, technology, skills, distribution channels, etc.). Competitive advantage arises as a result of these linkages and the Marshallian idea that geographic proximity creates the type of collaborations, knowledge spillovers, and positive externalities that firms can use and exploit (De Langen, 2002). These externalities are based on the presence of qualified labor, production inputs (e.g., support services), and benefits stemming from industrial technological advancement (De Langen, 2002; Lazzeretti & Capone, 2010).

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Benito, G. R., Berger, E., De La Forest, M., Shum, J. (2003). A cluster analysis of the maritime sector in Norway. International Journal of Transport Management 1:  205–​206. Bryan, J., Munday, M., Pickernell, D., Roberts, A. (2006). Assessing the economic significance of port activity: evidence from ABP Operations in industrial South Wales. Maritime Policy and Management 33: 371–​386. Cai, J., Leung, P., Minling, P., Pooley, S. (2005). Economic linkage impacts of Hawaii’s longline fishing regulations. Fisheries Research 74: 232–​242. Cai, J., Leung, P. (2004). Linkage measures: a revisit and a suggested alternative. Economic Systems Research 16: 63–​83. Cai, J., Leung, P., Mak, J. (2006). Tourism’s backward and forward linkages. Journal of Travel Research 45: 36. Colgan, C.S. (2003). Measurement of the ocean and coastal economy: Theory and methods, NOEP, Publication, 3. Colgan, C. S. (2007). Measurement of the ocean and coastal economy: theory and methods, National Ocean Economics Project, USA. Colgan C. (2013). The ocean economy of the United States: Measurement, distribution and trends, Ocean and Coastal Management 71: 334–​343. Coffman, M., Kim, K. (2010). The economic impacts of banning commercial bottomfish fishing in the Northwestern Hawaiian Islands. Ocean and Coastal Management 52: 166–​172. Collier P. (2001). A Monograph Study of Offshore Fishing and Social Change in Kilmore Quay, Co. Wexford. Marine Resource Series, No. 15, Marine Institute, Ireland. Dietzenbacher E. (2002). Interregional Multipliers: Looking Backward, Looking Forward, Regional Studies 36(2): 125–​136. De Langen, P. W. (2002). Clustering and performance: the case of maritime clustering in the Netherlands. Maritime Policy & Management 29(3): 209–​221. Driffield, N., Munday, M., Roberts, A. (2005). Foreign direct investment, transactions linkages, and the performance of the domestic sector. International Journal of the Economics of Business 9: 335–​351. Han, S. Y., Yoo, S. H., & Kwak, S.J. (2004). The role of the four electric power sectors in the Korean national economy: An input–​output analysis. Energy policy 32(13):  1531–​1543. Hirschman, A.O. (1958). The Strategy of Economic Development. New York: Yale University Press. Kildow, J. T., McIlgorm, A. (2010). The importance of estimating the contribution of the oceans to national economies. Marine Policy 34: 367–​374. Kim, J. S., Sohn, B. A., & Whang, B. G. (2002). A tolerance approach for unbalanced economic development policy-​making in a fuzzy environment. Information Sciences 148(1): 71–​86. Kwak, S. J., Yoo, S. H., Chang, J. I. (2005). The role of the maritime industry in the Korean national economy: an input–​output analysis. Marine Policy 29(3): 371–​383. Lazzeretti, L., Capone, F. (2010). Mapping shipbuilding clusters in Tuscany: main features and policy implications. Maritime Policy & Management 37(1): 37–​52.

 



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Lenzen, M. (2003). Environmentally important paths, linkages and key sectors in the Australian economy. Structural Change and Economic Dynamics 14(1): 1–​34. McIlgorm, A. (2016). Ocean Economy Valuation Studies in the Asia-​ Pacific Region: Lessons for the Future International Use of National Accounts in the Blue Economy. Journal of Ocean and Coastal Economics, 2(2): 6. Midmore, P., Munday, M., Roberts, A. (2006). Assessing industry linkages using regional input-​output tables. Regional Studies 40(3): 329–​343. Miller, R. E., Blair, P. D. (2009). Input–​output analysis: Foundations and extensions. Cambridge University Press. Morrissey, K., O’Donoghue, C. (2012). The Irish marine economy and regional development. Marine Policy 36: 358–​364 Morrissey K., O’Donoghue C. (2013). The role of the marine sector in the Irish national economy: An input–​output analysis. Marine Policy 37, 230–​238. Morrissey, K., O’Donoghue, C., Hynes, S. (2011). Quantifying the value of multi-​ sectoral marine commercial activity in Ireland. Marine Policy 35: 721–​727. Norman-​López, A., Pascoe, S. (2011). Net economic effects of achieving maximum economic yield in fisheries. Marine Policy 35(4): 489–​495. Nuryadin, D., Syaifudin, N., Handika, R., Setyobudi, R. H., Udjianto, D. W. (2016). The Economic of Marine Sector in Indonesia. Aquatic Procedia 7: 181–​186. O’Donnchadha G., Callaghan T., Niland C. (2001) A Socio-​ economic Study of Fisheries in Counties Cork, Donegal, Kerry and Galway. Marine Resource Series, No. 11.Marine Institute, Dublin. Perez-​Labajo, C. (2001). Spending pattern of the recreational maritime sector and its impact on employment: the case of Cantabria, Spain. Marine Policy 25: 187–​196 Porter, M. E. (1990). The Competitive Advantage of Nations. MacMillan, London. Rasmussen, P. N. (1956). Studies in Intersectoral Relations. Amsterdam, North-​Holland. Rong-​Her Chiu & Yu-​Chang Lin (2012). The inter-​industrial linkage of maritime sector in Taiwan: an input–​output analysis, Applied Economics Letters 19(4): 337–​343. Saharuddin, A. H. (2001). National ocean policy—​new opportunities for Malaysian ocean development. Marine Policy 25: 427–​436. Sea Change Strategy. (2007). Sea Change: A Marine Knowledge, Research and Innovation Strategy for Ireland 2007–​2013. Marine Institute, Renvyle, Galway. Sigfusson, T., Arnason, R. & Morrissey, K. (2013). The economic importance of the Icelandic fisheries cluster—​Understanding the role of fisheries in a small economy. Marine Policy 39, 154–​161. Van Der Linden, J. A. (2001). The economic impact study of maritime policy issues: application to the German case. Maritime Policy & Management 28(1):  33–​54. Vega, A., Miller A., O’Donoghue. (2014). Economic impacts of seafood production growth targets in Ireland, Marine Policy 47: 39–​45.

 

 

Chapter Three

Accounting the Marine Economy Capturing Economic Change Through Time Series Data

3.1  INTRODUCTION Oceanic ecosystem services support a range of human benefits, and the need to empirically document these processes is well established in the physical sciences (Hawkins et al., 2013). Globally, extensive research networks are producing growing data sets in oceanography, fisheries and ocean observation that document in unprecedented detail the physical process underpinning the marine environment. Advances in marine instrumentation and data storage facilities also means that much of this data is time-​series in nature, thus building a database that allow insights into past and present trends and provides a platform for forecasting future events and testing policy interventions. This data is in turn being used to inform marine policy at the local, national and international level (Hawkins et al., 2013). However, at a time when the impact of human activities is increasingly seen as the biggest threat to the seas and oceans (Malone et al., 2010), similar data is not available for marine-​ based economic activities (Foley et al., 2014). For example, compiling, linking and harmonizing data on the Canadian ocean ecosystem, Cisneros-​ Montemayor et al. (2016) note that much of the data they captured related to single-​species fisheries. They further note the overall under-​representation of data on the economic and social aspects of the marine resource. Within the global economic arena, in the recent years, the importance of marine resources for economic development has come to the forefront, in particular, with the focus on the Blue Growth agenda and the Blue Economy (Foley et al, 2014; Morrissey, 2014). Countries such as China (Zhao et al., 2014) and institutions such as the EU (European Commission, 2012) see the ocean, or “Blue Economy” as an integral means of meeting these resourcing needs. To aid strategic decision making on the oceans and coastal regions, 43

 

44

Chapter Three

recent literature has focused on developing national economic indicators for the marine sector. To date, these assessments have used national level, cross-​ sectional indicators of economic activity such as gross domestic product (output), gross value added (GVA), employment and income estimates associated with marine resource activity (Colgan, 2013). However, while insightful in depicting the marine economy at one point in time, such analysis does not allow forecasting or projections of future changes in the sector. It further fails to measure the performance of the sector across time and during periods of economic change. In contrast, time series data can be used to underpin forecasts of economic activities based on historic events. This information can in turn be used to create adaptive, holistic based approaches to sustainable marine development, policymakers and planners require temporally referenced data on marine activities (Colgan, 2013; Morrissey, 2015). 3.2  DATA FOR ECONOMIC TREND ANALYSIS As noted in Chapter 1, data on marine activities can be classified into three broad categories (Morrissey et al., 2011): Type 1, Type 2, and Type 3 data (Morrissey et al., 2011). Type 1 data is data that is in the public domain. Such estimates are generally confined to those sectors whose connection to the sea is clear (i.e., commercial fisheries, coastal transportation). Type 2 data is data that is publicly collected but is not released into the public domain. National statistical agencies prepare a number of business censuses and surveys each year. Examples include the Annual Business Survey in the UK or the Census of Production in Ireland. This data is at a lower industrial (such as NACE, Europe; SIC, UK; NAICS, North America) or geographical classification and is considered confidential. However, as will be discussed later in the chapter, access can usually be granted to researchers interested in examining the data in a secure setting. Type 3 data is data that is not available in the public domain. In some cases it is possible to obtain data through sector reports and academic research papers. If such data is not available, the researcher has three choices: 1. Limit the valuation of the marine resource to the sectors for which Type 1 or Type 2 data is available (Morrissey, 2015) 2. Conduct a survey (Morrissey et al., 2011; Vega et al., 2013). 3. Estimate the values (Kalaydjian, 2009) To date, most research on the economic activities surrounding the marine resource have combined two or more of these data to provide an overall

 



Accounting the Marine Economy

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economic valuation of the marine economy, with each approach having its own particular advantages and disadvantages. For example, while a survey approach to obtaining Type 3 data will allow for a complete census of marine business, surveys are expensive and time consuming and require a business register of marine business on which to base the sample. The estimation or imputation of values for each sector allows for a statistically robust estimation of each sub-​sector. However, if time-​series data is based on these estimates the confidence intervals for each sector estimate become larger for each year. In contrast, many countries produce data as part of their National Accounts that contain data on some of the subsectors within the marine sector. As noted in Chapter 2, these subsectors usually include fisheries, water transport and oil and gas. However, further data on the marine economy is obtainable through secure data environments and data sharing agreements within most national statistic agencies (Colgan, 2013; Morrissey, 2015). Data agreements are required as public economic data series must generally conform to the rules concerning confidentiality, meaning that no data can be published from which it would be possible to determine the employment or wages of a single establishment (Colgan, 2013). This means that the finer the sector detail in either industry or geography the more likely the data is confidential. Within this context, data on sectors such as boat building, water construction, fisheries split between capture fisheries and aquaculture, seafood processing will usually available from national data offices through a secure data facility (Morrissey, 2015). However, data on sectors such as water-​based recreational activities or seaweed production would be deemed too sensitive to release. The advantage of using secondary data is that it is: 1. Free; 2. It is representative of its sector; 3. It allows comparison with between marine and non-​marine sectors; and 4. It has good temporal consistency as most of these datasets are provided on an annual basis. However, without augmenting this data with a survey or imputation techniques, information on a number of marine sector activities (e.g., coastal and marine tourism, marine engineering, etc.) that are considered in other international estimates of the marine sector will be missing. Thus, the use of secondary data reflects a trade-​off between comparability over time and precision (Morrissey, 2014). The remainder of this chapter provides a Case Study based on the work of Morrissey (2014) that examines the usefulness of time series data on the marine sector.

 

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3.3  CASE STUDY: TRENDS IN THE ENGLISH MARINE SECTOR: 2003 TO 2011 Human activities in the world’s oceans and coasts are at an unprecedented scale and expanding rapidly (Stojanovic & Farmer, 2013; Kildow & McIlgorm, 2010). As previously noted, the oceans have become a focal point for new activities including wind and wave power, marine biotechnology, marine technology and other enterprises. Data recording these changes across time are essential to sustainably plan future development initiatives in the oceans and public policy such as marine spatial planning. To demonstrate the usefulness of time-​series data to inform marine policy this chapter provides a trend analysis of the English marine economy between 2003 and 2011 using secondary data. Based on research by Morrissey (2014) this case study examines the output and employment performance of the marine sector over an eight-​year period with a view to future marine management policies with global economic trends as a backdrop. To examine the performance of the English marine sector across time this case study uses of an annual enterprise-​level dataset available through the Office of National Statistic’s for England and Wales, Virtual Microdata Laboratory. The Interdepartmental Business Register (IDBR) is a comprehensive database of UK businesses, drawn from administrative data sources (Evans & Welpton, 2009). The IDBR draws upon a number of administrative datasets including the Her Majesties Revenue and Customs (HMRC), Dunn and Bradstreet, Office of National Statistics surveys and Companies House. The use of the IDBR for research by a wider audience is restricted for two reasons. First, access is highly restricted due to the inclusion of highly confidential HMRC (Revenue and Customs) data. Second, it is difficult to perform historical analysis on the data. While the register is updated at regular intervals a regular set of reference changes are not made. This makes it difficult to build a longitudinal picture of businesses over their lifecycle. To resolve these issues the Business Structure Dataset (BSD) is produced. The BSD is an annual snapshot the IDBR with the Virtual Microdata Laboratory hosted by the Office of National Statistics (Evans & Welpton, 2009). The BSD “snapshot” is taken every March and includes data on enterprises and local units. Of interest to this paper is that the consistency of IDBR reference numbers throughout time enables the BSD to form a longitudinal dataset. The number of variables found in the BSD is small relative to other data sources. However, the BSD has extensive coverage, since any organization registered for VAT or PAYE is included (Evans & Welpton, 2009). Of interest to this Case Study is that the BSD contains enterprise level data by Standard Industrial Code (SIC) (2003 and 2007) on key economic indicators for the marine sector; including turnover or output, employment (including owners),

 



Accounting the Marine Economy

47

employees (excluding owners) and a number of enterprises. A spatial variable Government Office Region is also included in the BSD. Within the BSD, each enterprise is allocated a unique reference number and the BSD is designed to ensure that enterprise reference numbers consistently identify enterprises over time (Evans & Welpton, 2009). The Business Structure dataset is now increasingly used for academic and government studies (Evans & Welpton, 2009; Hijzen et al., 2010; Riley & Robinson, 2011; Riegler, 2012). Access to the BSD was gained via the Virtual Microdata Laboratory hosted by the Office of National Statistics.

Table 3.1  Matched 2003 and 2007 Sic Codes for the Marine Sector SIC 2003

SIC 2003 description

5010 5020 15201

Fishing Fish farming Freezing of fish

3110 3210 10200

15209

Other fish processing and preserving Passenger sea and coastal water transport Freight sea and coastal water transport Renting of passenger water transport equipment Renting of other water transport equipment Building and repairing of ships Building and repairing of ships Building and repairing of ships Building and repairing of pleasure and sporting boats Building and repairing of pleasure and sporting boats Freezing of fish

10200

Other fish processing and preserving Construction of water projects

10200

61101 61102 71221 71229 35110 35110 35110 35120

35120

15201 15209 45240

SIC 2007

50100 50200 77341 77342 33150 30110 33200 33150

SIC 2007 description Marine fishing Marine aquaculture Processing and preserving of fish, crustaceans and molluscs Processing and preserving of fish, crustaceans and molluscs Sea and coastal passenger water transport Sea and coastal freight water transport Renting and leasing of passenger water transport equipment Renting and leasing of freight water transport equipment Repair and maintenance of ships and boats Building of ships and floating structures Installation of industrial machinery and equipment Repair and maintenance of ships and boats

30120

Building of pleasure and sporting boats

10200

Processing and preserving of fish, crustaceans and molluscs Processing and preserving of fish, crustaceans and molluscs Construction of water projects

42910

 

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Accessing the BSD via the Virtual Microdata Laboratory, eight years of economic data were used including from 2003 to 2011. Using the SIC for 2003 (years 2003 to 2006) and 2007 (years 2007 to 2011), eleven marine sectors were identified within the BSD. Enterprises that were coded as inactive within the BSD were excluded from the analysis. Table 3.1 provides an overview of the SIC sectors identified as being marine based and the corresponding codes for SIC 2003 (applicable to years 2003 to 2006) and SIC 2007 (applicable to years 2007 to 2011). The sectors identified as belonging to the marine sector in the BSD included; marine fishing, marine aquaculture, seafood processing, passenger sea and coastal transport, freight sea and coastal transport, renting and leasing of passenger transport, renting and leasing of freight transport, building and repairing of ships and boats, building of pleasure boats, and water construction. These sectors are broadly the same as previous national level definitions of the marine sector with the exclusion of marine and coastal tourism (Kildow & McIlgorm, 2010; Morrissey et al., 2011; Colgan, 2013). The SIC codes contained in the BSD did not provide information on a number of marine sector activities, such as coastal and marine tourism, marine engineering, marine commerce, that are considered in other international estimates of the marine sector (Kildow & McIlgorm, 2010) nor the 2005 estimates of the English marine sector (Pugh, 2008). While the nonavailability of a full marine sector profile from the BSD is a limitation, Colgan (2013) notes that the information derived from trend analysis means that such analysis is still meaningful. Thus, a further objective of the case study is to demonstrate the effectiveness of using secondary data to understand the multiple human based uses of the marine resource across time. 3.4  RESULTS Using the Office of National Statistics BSD, table 3.2 provides the first temporal analysis of the economic performance of the English marine sector with regard to output and employment. From table 3.2, one can see that the freight transport (68 percent) and renting passenger transport (49 percent) recorded the largest increase in output during the pre-​recession period (2003–​2007). In contrast, the ship building-​repair (−18  percent) had the largest decrease in output during this period. Indeed, the ship building-​repair sector was the only marine sub sector to experience negative growth during the same period. Conversely, postrecession (2008–​2011), pleasure boat building (90 percent) and shipbuilding and repair (70 percent) experienced the highest increase in output. In contrast, freight transport (−1  percent) and renting passenger

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Table 3.2  Economic Performance of the Marine Economy Year on Year from 2003 to 2011 and between 2003–​2007 and 2008–​2013 2005

2006

0.04 −0.04

0.05 −0.02

−0.09 0.01

0.09 0.20

0.12 −0.03

−0.01 −0.02

2007

2008

2009

2010

2011

2003–​2007

2008–​2011

0.09 0.00

−0.13 0.00

0.08 −0.02

−0.03 −0.03

0.11 −0.02

0.08 −0.05

0.16 −0.08

−0.14 −0.05

0.00 −0.10

−0.27 −0.31

−0.01 0.08

0.09 −0.08

0.00 −0.02

0.05 −0.01

0.08 −0.02

0.06 −0.01

0.03 −0.03

0.08 −0.06

0.17 −0.02

−0.04 0.00

0.10 −0.04

0.04 −0.02

0.17 −0.13

0.09 −0.05

0.17 0.15

0.04 0.02

0.03 −0.12

0.09 0.07

−0.10 −0.17

0.18 0.03

0.36 −0.16

−0.02 0.13

0.36 0.10

0.57 0.02

0.12 0.15

0.11 0.13

0.71 −0.16

−0.50 −0.04

0.12 0.41

0.20 −0.41

0.35 −0.04

−0.07 0.02

0.06 0.06

0.51 −0.42

0.03 0.01

0.15 0.06

0.11 0.01

0.27 −0.10

−0.07 0.40

−0.03 −0.24

0.10 0.25

−0.08 −0.09

0.68 −0.03

Accounting the Marine Economy

Fishing Output (%) Employment Aquaculture Output (%) Employment Processing Output (%) Employment Water Construction Output (%) Employment Passenger Transport Output (%) Employment Freight Transport Output (%) Employment

2004

−0.01 −0.13 (Continued )

49

Renting Passenger Transport Output (%) Employment Renting Freight Transport Output (%) Employment Pleasure Boats Building Output (%) Employment Ship Build/​Repair Output (%) Employment Overall Output (%) Employment

Table 2.6  (Continued)

0.41 0.07 0.02 −0.09 0.06 0.04 −0.08 −0.17 0.05 −0.05

0.22 0.04

0.06 0.04

−0.07 −0.01

0.02 0.02

2005

0.00 −0.09

2004

0.17 0.00

0.10 0.04

0.10 0.15

−0.06 −0.05

−0.11 −0.04

0.03 −0.05

−0.94 −0.91

0.33 −0.01

2007

−0.03 0.00

0.00 −0.03

−0.38 −0.35

0.27 −0.1

−0.23 −0.19

2008

0.13 −0.03

0.38 0.07

0.65 0.47

0.31 −0.19

0.49 0.14

2009

0.14 0.02

0.09 0.07

0.12 −0.07

0.61 0.28

−0.17 0.14

2010

0.01 −0.01

0.14 0.00

0.03 0.06

−0.27 0.03

−0.20 −0.12

2011

0.18 −0.08

−0.16 −0.18

0.28 0.19

0.24 −0.09

0.49 −0.01

2003–​2007

0.30 −0.01

0.70 0.14

0.90 0.45

0.54 0.06

−0.01 0.15

2008–​2011

50

0.12 0.82

−0.21 0.02

2006

 

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transport (−1 percent) were the only two sectors to experience a drop in turnover between 2008 and 2011. In terms of employment, during the period 2003 to 2007, the pleasure boat building (19 percent) and water construction (10 percent) sectors experienced the largest increase in employment. In contrast, during the same period, the shipbuilding and repair (−18 percent) and seafood processing (−13 percent) sectors experienced the largest decline in employment. Post global economic recession, five sectors: pleasure boat building (45 percent), the renting of passenger transport sector (15 percent), ship building and repair (10 percent), renting freight transport (5 percent), water construction (2 percent) experienced growth in employment. While the passenger transport (−42 percent), freight transport (−13 percent), and fishing (−8 percent) sectors experienced the largest drop in employment across the marine economy. Examining the overall performance of the marine sector, table 3.2 indicates that turnover within the sector increased by 18 percent between 2003 and 2007. In contrast, employment in the overall sector decreased by 8 percent. Postrecession, the marine sector experienced a 30 percent increase in output, while employment in the sector decreased by 1 percent. Thus, this would indicate that in terms of output the English marine sector performed strongly postrecession as a whole. Furthermore, employment in the sector decreased at lower rate postrecession (1 percent) than in the run up to the recession (8 percent). From table 3.2, one can see that seven sectors saw a decrease in employment levels between 2008 and 2011. Indeed, in the overall marine economy, employment fell by 1 percent between 2008 and 2011. In contrast, only two sectors experienced a decrease in output, aquaculture and renting passenger transport. Thus, using the Office of National Statistics BSD, this analysis indicates that the postrecession time period was associated with a greater decline in employment rates in the marine sector compared to the output. Exploring this further, table 3.3 presents the change in the number of enterprises by marine subsector prerecession, and postrecession. From table 3.3 one can see that between 2003 and 2007, the water construction (24 percent), passenger transport (20 percent), and pleasure boat building (11 percent) sectors were the only three marine subsectors to experience a growth in the number of enterprises registered for VAT or PAYE. In contrast, table 3.3 shows that postrecession, only one sector, the freight transport sector experienced a growth in enterprises. Tying the analysis presented in table 3.2 to table 3.3, potentially indicates that the drop in employment levels across the marine subsectors from 2008 to 2011 is linked to the closure, rather than downsizing of marine sector enterprises. This hypothesis is further validated on examining column 3 in table 3.3. Column 3 presents the enterprise level average change in employment rates from 2008 to 2011. From table 3.3, one can see that three sectors, passenger transport

 

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Table 3.3  Change in Number of Marine Sector Establishments, 2003–​2007 and 2008 to 2011 and Change in Employment Rates Per Establishment 2008–​2011 Number of establishments Fishing Aquaculture Processing Water Construction Passenger Transport Freight Transport Renting Passenger Transport Renting Freight Transport Pleasure Boats Building Ship Build/​Repair

2003−2007

2008−2011

−0.05 −0.04 −0.08 0.24 0.20 −0.03 −0.12 −0.09 0.11 −0.01

−0.04 0.02 −0.02 −0.08 −0.07 0.11 −0.06 −0.19 −0.02 −0.04

Change in average employment rate 2008–​2011 −0.02 −0.04 0.05 0.06 −0.37 −0.22 0.17 0.27 0.46 0.22

(−37  percent), freight transport (−22  percent), and aquaculture (4  percent) experienced a decrease in employment levels, whilst the companies in six sectors (seafood processing, water construction, renting passenger transport, renting freight transport, pleasure boat building and ship building and repair) increased average employment numbers from 2008 to 2011. It should be noted that while fishing (−2 percent) did experience a decrease in average employment, the majority of fishing companies are sole fishers, and this represents a decrease in overall fishing activity. Examining the marine sector in the U.S. research found that whilst employment in the U.S. marine economy decreased post global recession, real GDP (output) increased by 44.9 percent (Colgan, 2013). Table 3.4 presents a comparison of output performance for the marine sector (as defined in this paper) and the national economy, disaggregated into sixteen SIC sectors from 2003 (base year) to 2010. Estimates for 2011 have not been released to date. From table 3.4, one can see that there is considerable variation in the short-​term economic performance between the marine sector and the national economy. Examining similar trends in the U.S. marine sector, recent research concluded this is because key sectors in the marine economy are relatively volatile on a short-​term basis (Colgan, 2013). The analysis presented in table 3.2 demonstrates this is also the case for the English marine economy. For example, from table 3.2 one can see that output in the fisheries sector increased by 9 percent in 2007, decreased by 13 percent in 2008 and again increased in 8 percent in 2009. While it is well documented that natural resource-​based sectors are very volatile in the short term, examining the pleasure boat building sector, a manufacturing sector,

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Table 3.4  Comparison of Output Performance for the Marine Sector and the National and 16 Sector Sic Level 2005

2006

2007

2008

2009

2010

0.17 0.13 −0.01 −0.04 0.07 0.09 0.05 0.02 0.06 0.06 0.10 0.07 0.04 0.05 0.08

−0.28 0.14 0.02 −0.01 0.08 0.04 0.02 0.04 0.04 0.01 0.20 0.03 0.08 0.06 0.09

0.13 0.06 0.02 0.25 0.11 0.08 0.05 0.04 0.06 0.03 0.03 0.02 0.09 0.06 0.04

0.02 −0.14 0.02 0.04 0.06 0.09 0.04 0.05 0.04 0.08 0.12 0.12 0.07 0.05 0.02

0.08 0.08 −0.03 0.00 0.04 0.01 −0.02 0.03 0.03 0.03 0.16 −0.03 0.03 −0.01 0.03

−0.01 −0.07 −0.04 0.13 0.04 −0.13 −0.01 −0.04 −0.03 −0.04 0.12 −0.16 −0.03 −0.02 0.04

0.08 0.00 0.07 0.02 0.09 −0.03 0.04 0.02 0.08 0.01 −0.07 0.17 0.03 0.05 0.02

0.06 0.09 0.06 0.09 0.03 0.06 0.02

0.07 0.07 0.04 0.05 0.06 0.05 0.05

0.08 0.07 0.04 0.04 0.03 0.05 0.17

0.06 0.03 −0.01 0.05 0.01 0.06 −0.06

0.05 0.04 0.00 0.00 0.12 0.02 −0.03

0.03 0.06 0.01 0.03 −0.08 −0.02 0.13

0.03 0.05 0.05 0.03 0.12 0.03 0.14

Accounting the Marine Economy

Agriculture, forestry and fishing Mining and quarrying Manufacturing Electricity, gas, steam and air-​conditioning supply Water supply; sewerage and waste management Construction Wholesale and retail trade; repair of motor vehicles Transportation and storage Accommodation and food service activities Information and communication Financial and insurance activities Real estate activities Professional, scientific and technical activities Administrative and support service activities Public administration & defence; compulsory social security Education Human health and social work activities Arts, entertainment and recreation Other service activities Activities of households National Economy Marine Sector

2004

Source: ONS Regional Accounts

53

 

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output increased by 3 percent in 2007, decreased by 38 percent in 2008 and increased by 65 percent in 2009. Post-​recession at the sectoral level, table 3.4 demonstrates that only three sectors, Agriculture, Forestry and Fishing (8 percent), Mining and Quarrying (8 percent), and finance and insurance activities (16 percent) out-​performed the marine sector. Indeed, the Agriculture, Forestry and Fishing sector encompasses a marine sector (fishing), so this comparison is somewhat biased. In 2009, one can see that with the exception of the Electricity, Gas, Steam and Air-​conditioning supply sector (13 percent), the output in the marine sector out-​performed (13 percent) all other SIC sectors. The same pattern is maintained in 2010, where only the real estate sector (17 percent) out-​performed output activity in the marine sector (14 percent). Table 3.5 examines the performance of the marine sector, and the national economy, disaggregated into sixteen SIC sectors prerecession (2003–​2007) and postrecession (2008–​2010). Overall, in the pre-​recession period output in

Table 3.5  Comparison of Output Performance for the Marine Sector, the National and 16 Sector Sic Level Pre-​Recession (2003–​2007) and Postrecession (2008–​2010) Sector Agriculture, forestry and fishing Mining and quarrying Manufacturing Electricity, gas, steam and air−conditioning supply Water supply; sewerage and waste management Construction Wholesale and retail trade; repair of motor vehicles Transportation and storage Accommodation and food service activities Information and communication Financial and insurance activities Real estate activities Professional, scientific and technical activities Administrative and support service activities Public administration and defence; compulsory social security Education Human health and social work activities Arts, entertainment and recreation Other service activities Activities of households National Economy Marine Sector Source: ONS Regional Accounts

2003–​2007

2008–​2010

−0.03 0.17 0.06 0.23 0.37 0.34 0.18 0.16 0.21 0.19 0.53 0.25 0.31 0.24 0.24

0.07 −0.07 0.03 0.15 0.14 −0.15 0.02 −0.02 0.05 −0.03 0.05 −0.01 0.01 0.02 0.07

0.30 0.29 0.14 0.25 0.13 0.24 0.18

0.06 0.11 0.07 0.06 0.03 0.02 0.28

 



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the marine sector (18 percent) was joint 14th with the Wholesale sector. The Financial and insurance activities sector (53 percent) had the highest output performance. Postrecession, table 3.5 indicates that the marine sector had the largest increase in output (28 percent), experiencing 13 percent more growth in output than the next highest performing sector, Electricity, Gas, Steam and Air-​conditioning Supply (15 percent). Overall, while the English marine sector underperformed relative to other sectors during 2003–​2007 (or the global boom years), the sector grew faster in the post global recession compared to other English industrial sectors. 3.5  USING TREND DATA ON THE MARINE ECONOMY FOR POLICY AND GOVERNANCE To mitigate future vulnerabilities to shocks and stressors produced by economic change, marine resource managers need to understand the impact of economic changes on the marine sector (Jeffers, 2013). Marine policy has always tried to encompass the type and level of economic activity associated with the use of the marine resource. However, the link between the oceans and the economy in the policy arena has always tenuous (Colgan, 2013). Using economic trend data sustainable natural resource governance and policy may be developed that are coupled to wider human based economic trends (Douvere, 2008). Such models are important given the increasing attention to marine spatial planning and the need to provide a link between the measurement of onshore economic activities and human uses of the oceans. Examining the performance of sectors over time and across specific time periods (for example, pre-​and postrecession) also provides a mechanism to identify the need for new or updated management programs. Globally, the majority of marine management has centered on fisheries resources. However, the trend analysis presented in section 3.3 demonstrates that at the sectoral level (table 3.2) and enterprise level (table 3.3, column 3) the greatest growth has occurred in the ship–​boat building and repair sector, particularly the pleasure boatbuilding sector. Pleasure boatbuilding is a land based marine sector that is becoming increasing high-​tech and high value. Policymakers must be aware of that the marine resource does not stop at the coastline and significant potential lies in marine based on land activities such as shipbuilding and maritime commerce. Marine policy also needs to understand how changes to the marine resource itself impact on society and the environment more broadly (Colgan, 2013). Incorporating data on human activity and land-​based marine activity as well as information on the natural processes associated with the marine resource

 

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provides a framework to increase output and employment in the sector in a sustainable, integrated manner. 3.6  CONCLUSIONS The majority of economic analysis available to marine policymakers and planners has been cross-​sectional to date (Colgan, 2013). The complex dynamics and physical–​human interactions of the marine resource requires that policy for the sector is underlined by a wide set of empirical indicators (O’Mahony et al., 2009; Colgan, 2013; Cisneros-​Montemayor et al., 2016), both cross-​ sectional and time series. The benefit of using trend data is that actual long-​ term changes in economic activities related to the marine sector can be used to inform marine policy. Importantly, this chapter found that while the English marine sector underperformed relative to other sectors during 2003–​2007 (or the global boom years), the sector grew faster postrecession compared to other economic sectors and the national economy as a whole. Measurements of the marine sector are an evolving field of research and like all economic indicators they are imperfect (Colgan, 2013). As noted in section 3.2, the BSD did not provide information on a number of marine sector activities (e.g., coastal and marine tourism, marine engineering, etc.) that are considered in other international estimates of the marine sector. Thus, the marine sector data described here reflects a trade-​off between comparability and precision. This means that the data series underestimates the marine sector as a whole, as previously defined for the UK (Pugh, 2008). However, the decision to provide a trend analysis based on the BSD means that it possible to provide an intertemporal analysis of commercial activity for the sector in England that may be used to inform future policy. BIBLIOGRAPHY Cisneros-​Montemayor, A. M., Cheung, W. W. L., Bodtker, K., Teh, L., Steiner, N., Bailey, M, Sumaila, U. R. (2016). Towards an integrated database on Canadian ocean resources: benefits, current states, and research gaps. Canadian Journal of Fisheries and Aquatic Sciences 999: 1–​10. Colgan, C. (2013). The ocean economy of the United States: Measurement, distribution and trends. Ocean and Coastal Management 71: 334–​343. Douvere, F. (2008). The importance of marine spatial planning in advancing ecosystem-​based sea use management. Marine Policy 32(5): 762–​771. European Commission (2012). Blue Growth—​Scenarios and drivers for Sustainable Growth from the Oceans, Seas and Coasts. European Commission, Brussels

 



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Evans, P., Welpton, R. (2009). Business structure database: The Inter-​Departmental Business Register (IDBR) for research. Economic and Labour Market Review 3(1):  71–​75. Foley, N., Corless, R., Escapa, M., Fahy, F., Fernandez-​ Macho, J., Gabriel, S., Gonzalez, P., Hynes, S., Kalaydjian, R., Moreira, S., Moylan, K., Murillas, A., O’Brien, M., Simpson, K., Tinch, D. (2014). Developing a Comparative Marine Socio-​Economic Framework for the European Atlantic Area. Journal of Ocean and Coastal Economics 1(1), Article 3. DOI: http://​dx.doi.org/​10.15351/​ 2373-​8456.1007 Hawkins, S. J., Firth, L. B., McHugh, M., Poloczanska, E. S., Herbert, R. J. H., Burrows, M. T., Sims, D. W. (2013). Data rescue and re-​use: recycling old information to address new policy concerns. Marine Policy 42: 91–​98. Hijzen, A., Upward, R., Wright, W. (2010). Job creation, job destruction and the role of small firms: firm-​level evidence for the UK. Oxford Bulletin of Economics and Statistics 72(5): 621–​646. Jeffers, J. (2013). Double exposures and decision-​making: adaptation policy and planning in Ireland’s coastal cities during a boom-​bust cycle. Environment and Planning A 45: 1436–​1454. Jin, D., Hoagland, P., Wikgren, B. (2013). An empirical analysis of the economic value of ocean space associated with commercial fishing Marine Policy 42:  74–​84. Kalaydjian, R. (2011). French Marine-​ related Economic data, 2009. Marine Economics Department, IFREMER, Brest, France. Kildow, J.T., McIlgorm, A. (2010). The importance of estimating the contribution of the oceans to national economies. Marine Policy 34: 367–​374. Koehn, Z., Reineman, J. K. (2013). Progress and promise in spatial human dimensions research for ecosystem-​based ocean planning. Marine Policy 42: 31–​38. Malone, T., Davidson, M., DiGiacomo, P., Gonçalves, E., Knap, T., Muelbert, J., Parslow, J., Sweijd, N., Yanagai, T., Yap, H. (2010). Climate change, sustainable development and coastal ocean information needs. Procedia Environmental Sciences 1: 324–​341. Morrissey, K., O’Donoghue, C., Hynes, S. (2011). Quantifying the value of multi-​ sectoral marine commercial activity in Ireland. Marine Policy 35(5): 721–​727. Morrissey, K. (2014). Using secondary data to examine economic trends in a subset of sectors in the English marine economy: 2003–​2011. Marine Policy 50: 135–​141. Morrissey, K. (2015). An inter and intra-​ regional exploration of the marine sector employment and deprivation in England. The Geographical Journal 181(3):  295–​303. Office of National Statistics. (2013). UK Regional Accounts, Office of National Statistics: London. O’Mahony, C., Gault, J., Cummins, V., Kopke, K., O’Suilleabhain, D. (2009). Assessment of recreation activity and its application to integrated management and spatial planning for Cork Harbour, Ireland. Marine Policy 33: 930–​937. Pugh, D. (2008). Socio-​economic indicators of marine-​related activities in the UK economy. The Crown Estate, London.

 

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Riley, R., Robinson, C. (2011). Skills and economic performance: the impact of intangible assets on UK productivity growth, UK Commission for Employment and Skills. Riegler, R. (2012). Fragmentation and integration: new evidence on the organisational structure of UK firms, Ph.D. dissertation, University of Nottingham. Stojanovic, T. A., Farmer, C. (2013). The development of world oceans & coasts and concepts of sustainability. Marine Policy 42: 157–​165. Vega, A., Corless, R., Hynes, S. (2013). Ireland’s ocean economy, SEMRU Report Series, ISSN 2009-​6933 (Online). Zhao, R., Hynes, S., He, G. S. (2014). Defining and quantifying China’s ocean economy. Marine Policy 43: 164–​173.

 

Chapter Four

The Marine Sector and the Regions

4.1  INTRODUCTION Chapters 1–​3 described the poor understanding that the economic impact of the marine economy at the national level. However, recent research primarily among academics and consultants (McIlgorm, 2016) has resulted in a number of international studies that have estimated the value of the marine sector at the national level. These studies have found that the value of the marine economy contributes between x percent and x percent of national GDP. While perhaps overall a relatively small contribution to overall national GDP, there is an added assumption that marine activities, particularly seafood-​based activities are an important source of employment and income in poor coastal regions (Ross, 2013; Vega et al., 2014; Surís-​Regueiro, et al., 2014; Morrissey, 2015). Indeed, in Europe marine-​based activities are seen as an important instrument for channeling national and supranational funding to coastal areas (Surís-​ Regueiro et al., 2014; Morrissey, 2015). The rationale behind European policies focused on the fisheries and aquaculture sector is the assumption that fishing is an important economic activity in less developed coastal regions that have very few economic alternatives (Surís-​Regueiro et al., 2014). However, the impacts of three decades of globalization, a move to a single market in Europe and an increasing number of trade agreements between different economic blocs on the marine economy cannot be understated. As such the makeup of the marine sector is changing, which in turn presents challenges to some coastal communities forced to reshape their economic activities and diversify across the portfolio of marine industries (van Putten et al., 2016). The difficulty in maintaining traditions and safeguarding employment in coastal areas is particularly evident in the maritime transportation sector and the sea-​fisheries sector (Vivero, 2007; van Putten et al., 2016). Characterized 59

 

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as a low tech, resource constrained sector what role has the marine economy to offer the regions within this new global supply chain and distorting trade agreements? This characterization is further compounded by the drought of economic and social data on the marine economy at the regional level. Indeed, with the exception of research on the regional impact of the marine economy in Ireland (Morrissey & O’Donoghue, 2012), England (Morrissey, 2015) and China (Jiang et al., 2014) data limitations means that there has been little empirical analysis to examine the role of the marine sector in coastal regions. Within this context, this chapter examines the whether the marine sector, defined more broadly than the fisheries and aquaculture sector is a lifeline and a potential policy conduit in peripheral, coastal regions? Establishing the marine sector as a mobile, innovative, knowledge-​based sector, a case study of the Irish marine economy across the Irish regions is used to demonstrate that the marine sector is of benefit to coastal regions, but also to “core” urban areas. Following Sections… 4.2  THE GEOGRAPHY OF THE MARINE ECONOMY The tendency of activity and people to cluster in space has been discussed since the early 1800s (Von Thünen, 1826) however, it is only since the advent of what has come to be called New Economic Geography (NEG) (Krugman, 1999; Venables, 2006) that a dynamic and sustained interest has taken place on how to capture and endogenize the effect of “geography” on regional and national growth. Why do firms and industries concentrate geographically? An analytical starting point is the observation that to maximize profit firms seek to reduce inputs. Close proximity to factors of production (i.e., natural resources) and trade partners significantly reduces transaction and information costs. Thus, there is a rationale for interlinked industries to locate close to each other. Once established agglomerations become self-​ reinforcing; sustained via endogenous forces, which include localized technological spillovers, labor market pooling, nontraded inputs and positive backward and forward market linkages. The first three forces constitute technological externalities (Duranton & Puga, 2004). The fourth, market linkages, refer to externalities created due to market linkages within a sector (Gruber & Soci, 2010). The existence of positive market linkages and the interaction between increasing returns to scale and transaction costs implies that the establishment of a new firm within the agglomerate not only increases competition, but also enhances the profitability of existing firms (Titze et al., 2011). The underlying spatial dynamics of agglomerations implies that at any one point in time, some areas are more attractive than others. These “locational assets” draw mobile firms or sectors together, creating a core–​periphery

 



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structure, whereby one region ends up as the industrial core, attracting the majority of industrial production. The remaining regions become the periphery whose main role involves supplying natural resource-​based products to the core and importing industrial goods from the core region (Gruber & Soci, 2010). Within these models the natural resource sector, usually depicted as the agricultural sector is taken as immobile, without product differentiation, innovation or knowledge externalities (Gruber & Soci, 2010). However, in terms of the multisectoral marine economy outlined in ­chapters  1–​3, this chapter argues that such a hypothesis is not true. While agriculture does have a specific characteristic of being bound to the land and the ownership of land, property rights do not bind the marine economy. Chapters 1 and 2 have already outlined that companies producing marine-​ based goods and services may only indirectly consume–​supply the marine resource within their process of production. For example, companies involved in providing maritime insurance, a global, high value-​added sector, are located in the most densely populated cities in the world, Singapore, Hong Kong, and London. Indeed, the majority of industries, given their downstream or indirect intersectoral linkages with the marine are not tied geographically to the resource itself. With regard to location, Hynes and Farrelly (2012) in Ireland and Colgan, (1997) and Kildow and McIlgorm (2010) in the United States highlight that, marine-​based companies are located both in coastal and noncoastal areas and within peripheral and nonperipheral areas. The marine sector is not the same as the coastal economy, nor are all the sectors within the scope of the marine sector necessarily coastal Thus, firms within the marine economy are inherently more footloose than firms within the agriculture sector and as such may relocate away from peripheral locations if deemed economically advantageous. Furthermore, examining the employment profile of the marine economy, defining the marine economy as a peripheral economic activity inherently assumes that the employment profile for the sector is low skilled, natural resource based and wholly located in coastal areas. However, this assumption belies the increasingly high-​tech, service oriented nature of the sector (Morrissey et al., 2011). While in terms of sectoral innovativeness and competitiveness the EU’s Blue Growth Study (2012) identified ocean energy, coastal protection and blue biotechnology, all high-​tech, research intensive sectors as three of the twenty-​seven marine subsectors with the most promising global potential. To this end, a number of papers have sought to examine if it is advantageous for marine-​based industries to create regional agglomerations around the actual marine resource. In Finland, a study examining the regional socio-​economic impact of the fisheries industry found that while fishing activity is widely dispersed along the marine resource, the downstream industries—​ processing and

 

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wholesale—​are highly concentrated in two regions, where the fishing industry itself is most concentrated (Virtanen et al., 2001). These results confirm findings on the strong significance of buyer (processor/​wholesaler)–​supplier (fishers) links within the sea-​fisheries industry (Midmore et al., 2006). In Norway, research defined the sector as a regional industrial agglomeration and using econometrical techniques research found significant economies of scale across nine marine industries (Knarvik & Steen, 1999). From this analysis, it was concluded that the strength of intersectoral linkages ensures that the regional, coastal location of the marine companies will be self-​reinforcing and specific regional policy must be devised for the sector to evolve further (Knarvik & Steen, 1999). In Norway, further research examined the influence of regional agglomeration externalities on productivity in the salmon aquaculture sector (Tveteras & Battese, 2006). This research also found that regional agglomeration forces had a strong positive effect on productivity in the sector and that policy must be devised to enable further clustering of the sector. Recent research has therefore indicated the positive effect regional agglomeration around the marine resource and other downstream, interlinked industries may have on the sector as a whole. In terms of the marine economy, this indicates that although certain sectors do not require proximity to the marine resource and their location of production is mobile, vertical linkages between sectors and cost reductions mean that there is an economic rationale for marine industries to cluster around the marine resource and other marine-​ based industries. The contemporary focus on Marshallian agglomeration economies and the demonstrated success of spatial concentrations of marine-​ based industrial activities highlighted above, indicate that the marine sector may have an important and sustainable impact on regional development. 4.3  CASE STUDY: THE MARINE ECONOMY AND THE IRISH REGIONS Within Ireland, regional issues have historically attracted considerable attention, much of which centers on the size of Dublin and its perceived dominant share of the national economy (Moylan, 2011). Indeed, there is a perception that any economic success prior to the 2007 recession was centred within the Greater Dublin Area (GDA) and served to increase rather than addressed regional disparities (O’Leary, 2002). Table 4.1 provides an overview of regional performance for Ireland in 2007. Given this perception, the natural resource sector, particularly the agricultural sector is seen as a key lifeline in rural and coastal Ireland. However, the Irish emphasis on agriculture as a rural lifeline is perhaps surprising in an international context. Consisting of

newgenrtpdf

 



Table 4.1  Key Characteristics of the Eight Nuts 3 Regions in Terms of Size and Economic Development. Indices of GVA per capita 2007**

481 260 419 1,160 1,210 497 365 474 632 3,178 4,338

70.1 65.8 70.6 69.3 141.2 78 84.6 73.4 123.5 111.2 100

Indices of Income per capita 2007** 92.3 91.2 93.6 92.5 111.7 103.7 97.6 93.4 95.7 102.9 100.0

Unemployment Rate*

GVA % 2007**

Persons at Work % 2007*

5.5% 4.4% 3.9% 4.7% 4.6% 3.6% 5.1% 4.9% 4.1% 4.5% 4.5%

7.8% 3.9% 6.8% 18.5% 27.9% 11.4% 8.4% 10.9% 14.6% 81.5% 100%

10.5% 5.8% 9.4% 25.7% 29.3% 11.9% 8.2% 10.5% 14.4% 74.3% 100%

The Marine Sector and the Regions

Border Midlands West Border, Midlands, West Region Dublin Mid-​East Mid-​West South-​East South-​West Southern and Eastern Region State

Population (‘000) 2007*

Source: Morrissey & O’Donoghue, 2012) *QNHS, 2007, ** National Accounts, 2007, CSO.

63

 

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Figure 4.1.  The Real Map of Ireland Source: Marine Institute.

900,000 km2 of seabed and 1,448 km of coastline (Cooper, 2009), the Irish marine resource is more than ten times the size of the Irish land resource (­figure  4.1). Furthermore, of the eight NUTS3 regions in Ireland, seven have a coastal border (Hynes & Farrelly, 2012). Economically, Ireland depends heavily upon its maritime transportation sector with 95 percent of the value and 99 percent of its trade transported by sea (Shields et al., 2005). However, the growth of nationalism in Ireland in the nineteenth century tended to prioritize the interests of people who worked the land rather than those who lived off the sea (Mac Loughlin, 2013). As such, from a cultural, economic and political perspective, marine activities, particularly fisheries were seen as the “poor sister” to agriculture in Ireland. Thus, little emphasis has been placed on the development of the marine sector in Ireland. However, as outlined in ­chapters  1 to 3, at an international level, the realization that the world’s oceans play an important role in climate regulation and many territory activities, notably food production, coupled with economic changes and the rapid advancement in ocean technology have seen a shift in the perception of the importance of the marine resource. Within Ireland specifically, two important events have meant that there has been an increasing emphasis on the role the marine activity can play in developing Irish

 



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regions. First the establishment The Marine Institute as the national agency responsible for advice on and implementation of marine research, technology, development, and innovation policy in 1991 developed an institutional platform for the marine economy. Second, the increasing regional divide between Dublin and “the rest of the country” meant that Irish policymakers had to look beyond agriculture as a means of sustaining rural and coastal economies in Ireland. The Role of the Marine Economy in the Irish Regions Using the definition of a multisectoral marine economy and adapting a methodology devised by the National Ocean Economic Program to value the U.S. marine economy (Colgan, 2003), it was found that in 2007, the Irish marine sector provided €1.44 billion in Gross Value Added (GVA) to the Irish economy and employed approximately 17,000 (Morrissey et al., 2011). Table 4.2 provides an overview of the sectors that are defined as marine based within the Irish economy. However, of interest to this chapter on the marine economy and the regions is that unlike previous estimates of the Irish and international marine sectors, the database created to estimate the national economic value of the sector, may be disaggregated to the regional level (Morrissey & O’Donoghue, 2012). Using this database, Morrissey and O’Donoghue (2012) produced the value of the marine sector for each of the eight NUTS3 regions in Ireland. The remainder of this case study provides an overview of Morrissey and O’Donoghue (2012) findings on the regional impact of the marine sector in Ireland using data from 2007. A discussion will also be offered on the sector’s ability to readdress the issue of regional imbalance in GVA, employment and productivity found in Ireland. Table 4.3 presents the contribution of the marine economy to each region in terms of GVA. From table 4.3, one can see that the South West and Dublin derive the largest proportion of marine-​based GVA, €393 million (26 percent), and €372 (27 percent) million, respectively. The Midlands, followed Table 4.2  Marine Subsectors in Ireland Marine services sector

Marine resources sector

Marine manufacturing

Ship Owners Port & Maritime Logistics Marine Tourism International Cruise High Tech Services Marine Commerce Other Services

Fisheries Aquaculture Seafood Processing Seaweed & Biotechnology Oil & Gas Renewable Energy

Boat Building Marine Construction Marine Engineering Other Marine Manufacturing

 

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Table 4.3  Regional Breakdown of Gva, Percentage of Marine Gva as a Percentage of Total Regional Marine Gva and, as a Percentage of Total Regional Gva

Border Midland West BMW Dublin Mid-​East Mid-​West South East South West SE State

Turnover

GVA

Regional Marine GVA % of total National Marine GVA

398 15 522 935 908 96 158 298 873 2333 3268

167 8 258 433 372 40 73 128 393 1006 1439

12% >1% 18% 30% 26% 3% 5% 9% 27% 70% 100%

Regional Marine GVA as a % of Total Regional GVA 1.3% 0.1% 2.2% 1.3% 0.5% 0.2% 0.6% 0.9% 1.3% 0.7% 0.8%

by the Mid-​East have the lowest proportion, €8 million (>1 percent), and €40 million (3 percent), respectively. This would seem to indicate the two regions with the greatest share of national GVA (see table 4.1), also have the largest share of marine-​based GVA. However, when one examines the relative share of marine GVA as a percentage of total regional GVA this relationship changes. From table 4.3, one can see that the West (2 percent), Border (1 percent), and South West (1 percent) derive the highest percentage of regional GVA from the marine. In contrast, the Midlands and Dublin derive the lowest. Table 4.3 indicates that the two regions with the highest national GVA, Dublin and the South-​West, also have the highest marine-​based GVA, in absolute terms. Thus, it would appear that the marine economy does not rebalance regional inequity in Ireland in terms of GVA. Table 4.4 presents a breakdown of the percentage share of each sector, manufacturing, resources, and services in overall regional marine-​based GVA. As one can see, in most regions (particularly Dublin) marine GVA is derived primarily from the services sector. Indeed, only in the Midlands (manufacturing) is the highest percentage of GVA not from the marine service sector. However, it is important to note that GVA derived from marine resources and marine services are equal in the Border region. The importance of marine resources in the Border region is due to the relative dominance of Killybegs fishing port in County Donegal and its environs in terms of fishing activity. Thus, fishing remains an important sector in the Border region. Table 4.4 also provides the percentage breakdown of the number of marine-​based service companies by region. Service industries have much

 



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Table 4.4  Percentage Share of Each Marine Sector in Overall Regional Marine-​Based Gva & Percentage Breakdown of the Percentage of Marine Services Businesses by Region

Border Midlands West Dublin Mid-​East Mid-​West South-​East South-​West

Manufacturing

Resources

Services

Percentage of Marine Service Businesses by Region

5% 97% 26% 5% 5% 1% 5% 4%

47% 3% 22% 5% 7% 5% 18% 38%

48% 1% 52% 90% 87% 94% 77% 58%

12% >1% 12% 27% 4% 9% 8% 27%

low input costs compared to manufacturing and resource-​based sectors. Thus, they generally have higher levels of GVA, compared to other traditional sectors. As one can see, at the regional level, both Dublin and the South West have the highest percentage of marine service-​based companies (27 percent). Thus, the gap in marine GVA between regions is accountable to the large proportion of service sectors in Dublin and the South West compared to other regions in Ireland. Examining the regional breakdown of marine GVA, this section found that marine services drive the economic impact of the marine sector at the regional level. However, it is important to note that marine resources provide a significant share of marine-​based GVA in the Border and South West regions. The Irish Regional Marine Economy: Labor Market Indicators With regard to employment Morrissey and O’Donoghue found that the South West (4,096 FTE) and the West (3,460 FTE) have the highest share of regional employment. The midlands (299 FTE) and Mid-​East (426 FTE) have the lowest levels of marine-​based employment. Examining the relative regional share of marine employment as a percentage of regional employment, this relationship is maintained. The South West and West have the highest percentage share (3 percent and 1 percent, respectively), while the Midlands and Mid-​ East have the lowest. Also, unlike the regional output indicator presented in table 4.3, Dublin is not the dominant region in terms of marine-​based employment. This relationship is again evident when one examines regional employment levels in the marine sector as a percentage of total regional employment. This indicator shows that the West and South-​West derive the highest level of regional employment, and Dublin the lowest. Again, however, the low relative share of marine-​based employment as a percentage of total regional employment for Dublin is related to the size of Dublin’s employment market,

 

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Table 4.5  Regional Breakdown of Fte Marine Employment, Percentage of Marine Employment as a Percentage of Total Regional Marine Employment and, as a Percentage of Total Regional Employment FTE Border Midland West BMW Dublin Mid-​East Mid-​West South East South West SE State

2,856 299 3,460 6,615 2,733 426 856 1,590 4,096 9,701 16,316

Regional Marine FTE as a % of National Marine FTE

Regional Marine FTE as a % of Regional FTE

18% 2% 21% 40% 17% 3% 5% 10% 25% 59% 100%

1.2% 0.2% 1.7% 1.2% 0.4% 0.1% 0.4% 0.7% 1.3% 0.6% 0.7%

Table 4.6  Percentage Share of Each Marine Sector in Overall Regional Marine-​Based Fte Employment

Border Midlands West Dublin Mid-​East Mid-​West South-​East South-​West

Manufacturing

Resources

Services

8% 92% 16% 9% 11% 2% 7% 6%

54% 7% 52% 11% 18% 11% 31% 38%

39% 1% 32% 81% 71% 86% 63% 56%

rather than low levels of marine-​related employment. This finding therefore confirms the results on regional employment presented in table 4.1 and previous research on Irish regional employment patterns that regional disparities exist to a lesser extent in terms of personal income and employment levels compared to GVA (table 4.5). To examine further what is driving the marine regional employment level, table 4.6 presents a breakdown of the percentage share of each sector—​ manufacturing, resources, and services—​in overall regional marine-​based employment. In five regions (particularly the Mid-​East and Dublin) marine employment is highest within the services sector. However, unlike marine GVA, three regions have higher employment levels in nonservice sectors. Employment in the Border (54 percent) and the West (52 percent) regions is highest in the resource sector, while marine-​based employment is dominated

 



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by manufacturing in the midlands. As with regional marine GVA, the importance of the marine resources sector for regional employment in the Border region is driven by Killybegs port and its environs. In the West, marine-​based employment is spread across the fishing, aquaculture, and seaweed sector. Given its relative distance from the coast, it is unsurprising that employment in the midlands region is dominated by marine manufacturing. It is interesting to note from this analysis that in terms of regional marine employment, the comparatively less well developed Border, Midlands, and West region, is dominated by nonservice sectors compared to the South-​Eastern region. Thus, in terms of regional development, marine resources, including commercial sea fishing, aquaculture, seaweed, marine-​based energy, and traditional manufacturing remain an importance source of marine employment in less developed regions in Ireland. The Irish Regional Economy: Productivity Market Indicators Productivity is the key component of growth within an economy and as such is an important determinant of output, competitiveness and living standards. Productivity may be defined as the rate of inputs to outputs within a company, industry or economy. Increases in productivity, allow firms and sectors achieve higher levels of value added within the broader economy and increase their competitiveness in both Ireland and internationally. There are two generally accepted measures of productivity: labor productivity and total factor productivity (TFP). Whereas labor productivity measures economic output per unit of labor, TFP relates output to the combined usage of factor inputs, namely labor and capital (Cassidy, 2004). This section focuses on regional labor productivity within the marine sector and compares it to overall regional labor productivity. Table 4.7 provides a comparison of regional marine-​based productivity (marine GVA per marine FTE) to overall regional productivity. From table 4.7, one can see that productivity in regional marine-​based sector is higher than overall regional productivity in the West (+€17,061), Dublin (+€29,636), Mid-​East (+€34,118) Mid-​West (+€16,749) and South East (€+19,662). One can see that Dublin had the highest levels of marine productivity in 2007, which is consistent with overall regional levels of productivity. At the national level, the productivity rate in the marine sector (€84,196 per FTE) is greater compared to the overall national rate (€79,345 per FTE). However, the difference between the national and marine rate of productivity (−€4,850) is significantly smaller than the differences observed across each region. To examine further what is driving marine regional productivity ­levels, table 4.8 presents a breakdown of the percentage share of each

 

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Table 4.7  Overall Regional and Regional Marine-​Based Productivity

Border Midland West BMW Dublin Mid-​East Mid-​West South East South West SE State

Marine productivity

Regional productivity

Difference

€58,473 €26,756 €74,566 €65,457 €136,114 €93,897 €85,280 €80,503 €95,947 €103,701 €84,196

€58,959 €54,364 €57,505 €57,287 €106,478 €59,779 €68,532 €60,841 €99,096 €86,971 €79,345

−€486 −€27,608 €17,061 €8,170 €29,636 €34,118 €16,749 €19,662 −€3,148 €16,729 €4,851

Table 4.8  Marine Productivity by Region and Sector

Border Midland West Dublin Mid-​East Mid-​West South East South West

Manufacturing

Resources

Services

Marine productivity

€42,802 €28,763 €93,290 €72,099 €46,844 €58,000 €63,261 €66,007

€50,924 €11,000 €24,322 €63,540 €36,434 €36,030 €47,325 €95,489

€71,650 €14,750 €95,076 €152,443 €115,384 €92,067 €99,037 €99,336

€58,473 €26,756 €74,566 €136,114 €93,897 €85,280 €80,503 €95,947

sector—​manufacturing, resources, and services—​in overall regional marine-​ based productivity. Examining the three subsectors, one can see that productivity is highest in the marine service sector across all regions in 2007. This result is consistent with the national level analysis of marine productivity (Morrissey et al., 2011), which found that marine services had the highest levels of productivity in 2007. At both the regional and sectoral level, it is the lower rate of productivity within the more labor-​intensive marine resource and the manufacturing sector that drives down productivity within the broader regional marine sector. 4.4  DISCUSSION The background for this chapter is based on the increased acknowledgement of the importance of the regions in providing a foundation for national economic growth (Moylan, 2011), the marine sector’s role in developing

 



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peripheral areas (Vega et al., 2014) and the recognition that the development of the marine resource requires a coherent set of indicators detailing the economic impact of the sector at the national and regional level (Böhnke-​ Henrich et al., 2013; Ban et al., 2013). To date, the relative economic role of the marine sector on regions has not been empirically addressed in the current Irish or international regional literature. However, a number of international papers have found that the although certain marine sectors do not require proximity to the marine resource and that their location of production is mobile, vertical linkages between each sector, and increased cost reductions mean that there is an economic rationale for marine industries to cluster around the marine resource. Within this context, one can argue that given the strong agglomeration effects demonstrated by international marine sectors there is a rationale for stand-​alone regional policy to focus on the multisectoral marine sector as a potential industry with strong regional comparative advantages. This rationale is further strengthened by the findings of this Chapter. First, although previous research found that the marine sector provided €1.44 billion or 1 percent of GVA to the Irish economy in 2007 (Morrissey et al., 2011), the Case Study presented in this Chapter found that the marine sector makes a greater contribution to the regional economy, accounting for 2.2 percent of GVA in the West, 1.3 percent in both the Border and South West region, while the contribution of employment followed regional GVA. Second, although relatively more important in the more peripheral Border, Midlands and West regions, this Case Study shows that in highly developed urban area such as Dublin, marine activities can be relevant with regard to their contribution to the GDP and employment particularly in the marine services sector. Finally, this Case Study that in terms of regional development the marine resources sector; commercial sea fishing, aquaculture, seaweed, marine-​based energy, remain an importance source of marine employment in less developed regions In Ireland. 4.5  CONCLUSIONS Geographically, the marine sector has traditionally been seen as being part of the peripheral economy; located in coastal areas characterized by low levels of employment and production opportunities and high levels of deprivation. However, the first section of this chapter aimed to redefine the marine sector as a high tech, high value sector with potential both in core and peripheral areas. Using a case study from Ireland, this chapter found that the marine economy offers economic opportunity in highly urban areas as well as peripheral areas. Within this context, one can argue that there is a rationale

 

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for stand-​alone regional policy to focus on the multisectoral marine sector as a potential industry with strong regional comparative advantages. However, in terms of welfare, the chapter also indicates that it is important that measures to aid coastal areas are not only filtered through marine policies as certain coastal areas may not have a marine sector and vice versa the marine activity may not be located in coastal areas. Finally from a methodological perspective, to evaluate the impact of funds such as the EMFF or marine policy, such as Marine Spatial Planning (MSP) policymakers require a range of data, economic (GVA, output, exports) and socio-​economic (employment, area level deprivation) and methods and techniques capable of estimating the impact of the sector across various spatial levels (Morrissey et al., 2014; Stojanovic et al., 2010). BIBLIOGRAPHY Ban, N. C., Bodtker, K. M., Nicolson, D., Robb, C. K., Royle, K., & Short, C. (2013). Setting the stage for marine spatial planning: Ecological and social data collation and analyses in Canada's Pacific waters, Marine Policy 39: 11–​20. Böhnke-​Henrich, A., Baulcomb, C., Koss, R., Hussain, S., de Groot, S. (2013). Typology and indicators of ecosystem services for marine spatial planning and management, Journal of Environmental Management 130: 135–​145. Cassidy, M. (2004). Productivity in Ireland: Trends and issues. Central Bank Quarterly Bulletin, The Central Bank, Dublin; Spring. Colgan, C. S. (1997). Estimating the value of the ocean in a national income accounting framework, preliminary estimates of gross product originating for 1997. National Ocean Economics Project, Working Paper 1. Colgan, C.S. (2003). Measurement of the ocean and coastal economy: Theory and methods, NOEP, Publication, 3. Colgan, C. (2013). The ocean economy of the United States: Measurement, distribution and trends. Ocean and Coastal Management 71: 334–​343. Cooper J. (2009). Coastal economies and people. Marine Climate Change Ecosystem Linkages Report Card. Cicin-​ Sain, B., Knecht, R. W. (1998). Integrated Coastal and Ocean Management: Concepts and Practices. Island Press, Washington, DC. Douvere, F. (2008). The importance of marine spatial planning in advancing ecosystem-​based sea use management. Marine Policy 32(5): 762–​771. Duranton, G., Puga, D. (2004). Micro-​ foundations of urban agglomeration economics. In Henderson, J.V., Thisse, J.F. Eds. Handbook of Regional and Urban Economics, Vol. 4, North-​Holland, Amsterdam, pp. 2063–​2117. Gruber, S., Soci, A. (2010). Agglomeration, agriculture, and the perspective of the periphery. Spatial Economic Analysis 5(1): 43–​72. Hynes, S., Farrelly, N. (2012). Defining standard statistical coastal regions for Ireland. Marine Policy 36: 393–​404.

 



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Jiang, X. Z., Liu, T. Y., Su, C. W. (2014). China‫׳‬s marine economy and regional development. Marine Policy 50: 227–​237. Jin, D., Hoagland, P., Wikgren, B. (2013). An empirical analysis of the economic value of ocean space associated with commercial fishing. Marine Policy 42: 74–​84. Kalaydjian, R. (2011). French Marine-​ related Economic data, 2009. Marine Economics Department, IFREMER, Brest, France. Kildow, J.T., McIlgorm, A. (2010). The importance of estimating the contribution of the oceans to national economies. Marine Policy 34: 367–​374. Knarvik, K. H. M., Steen, F. (1999). Self-​reinforcing Agglomerations? An empirical industry study. The Scandinavian Journal of Economics 101(4): 515–​532. Koehn, Z., Reineman, Kittinge, J. (2013). Progress and promise in spatial human dimensions research for ecosystem-​based ocean planning. Marine Policy 42: 31–​38. Krugman, P. (1991). Increasing returns and economic geography. Journal of Political Economy 99: 483–​499. O’Leary, E. (2002). Sources of regional divergence in the Celtic tiger: Policy responses, Journal of the Statistical and Social Inquiry Society of Ireland, 32, 1–​31. MacLoughlin J. (2010). Troubled Waters: A Social and Cultural History of Ireland’s Sea Fisheries. Dublin: Four Courts Press. McIlgorm, A. (2016). Ocean Economy Valuation Studies in the Asia-​ Pacific Region: Lessons for the Future International Use of National Accounts in the Blue Economy. Journal of Ocean and Coastal Economics 2(2): 6. Midmore, P., Munday, M., Roberts, A. (2006). Assessing industry linkages using regional input-​output tables. Regional Studies 40(3): 329–​343. Morrissey K. (2015). An inter and intra-​ regional exploration of the marine sector employment and deprivation in England. The Geographical Journal 181(3):  295–​303. Morrissey, K., O’Donoghue, C., Hynes, S. (2011). Quantifying the value of multi-​ sectoral marine commercial activity in Ireland. Marine Policy 35(5): 721–​727. Morrissey, K., O’Donoghue, C. (2012). The Irish marine economy and regional development. Marine Policy 36: 358–​364. Morrissey, K., O’Donoghue, C., Farrell, N. (2014). The local impact of the marine sector in Ireland: A spatial microsimulation analysis. Spatial Economic Analysis 9(1):  31–​50. Moylan, K. (2011). Irish regional policy; In Search of Coherence. Issues in Public Administration, Institute of Public Administration, Dublin. O’Mahony, C., Gault, J., Cummins, V., Kopke, K., O’Suilleabhain, D. (2009). Assessment of recreation activity and its application to integrated management and spatial planning for Cork Harbour, Ireland. Marine Policy 33: 930–​937. Ross, R. (2013). Exploring concepts of fisheries ‘dependency’ and community’ in Scotland. Marine Policy, pp. 55–​61. Shields Y., O’Connar J., O’Leary J. (2005). Ireland’s Ocean Economy and Resources. Marine Institute, Renville, Oranmore, Co. Galway. Stojanovic, T. A., Green, D. R., Lymbery, G. (2010). Approaches to knowledge sharing and capacity building: The role of local information systems in marine and coastal management. Ocean & Coastal Management 53(12): 805–​815.

 

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Surís-​Regueiro, J. C., Garza-​Gil, M. D., Varela-​Lafuente, M. M. (2014). Socio-​ economic quantification of fishing in a European urban area: The case of Vigo. Marine Policy 43: 347–​358. Titze, M., Brachert, M., Kubis, A. (2011). The identification of regional industrial clusters using qualitative input-​output analysis (QIOA). Regional Studies 45(1):  89–​102. Tveteras, R., Battese, G. E. (2006). Agglomeration externalities, productivity, and technical inefficiency. Journal of Regional Science 46(4): 605–​625. van Putten, I., Cvitanovic, C. & Fulton, E.A. (2016). A changing marine sector in Australian coastal communities: An analysis of inter and intra sectoral industry connections and employment. Ocean & Coastal Management 131: 1–​12. Vega, A., Miller, A., O’Donoghue, C. (2014). Economic impacts of seafood production growth targets in Ireland. Marine Policy 47: 39–​45. Venables, A. J. (2006). Equilibrium locations of vertically linked industries. International Economic Review 37: 341–​359. Virtanen, J., Ahvonen, A., Honkane, A. (2001). Regional socio-​economic importance of fisheries in Finland. Fisheries Management and Ecology 8: 393–​403. Vivero J. (2007) The European vision for oceans and seas—​social and political dimensions of the green paper on maritime policy for the EU. Marine Policy 31:409–​14. Von Thünen, J. (1826). The Isolated State. Pergamon Press, London.

 

Chapter Five

The Economic Impact of the Marine Sector on the Regions A Location Quotient Approach

5.1  INTRODUCTION Chapters 2 and 3 indicated that the economic importance of the marine sector in industrialized countries tends to be low, between 1 percent and 10 percent of nations’ GDP (Kwak et al., 2005; Kildow & McIlgorm; 2010; Morrissey et al., 2011). However, ­chapter  4 outlined a case study by Morrissey and O’Donoghue, (2012) which found that the marine economy as a whole is still significant at the regional level. Further research also demonstrates that specific subsectors within the marine economy, such as fisheries (Eggart & Tveterås, 2013; Midelfort-​Knarvik & Steen, 2002; Virtanen et al., 2001; Sigfusson et al., 2013) and shipping (Benito et al., 2003) can have a significant impact on regional and coastal economies (Surís-​Regueiro et al., 2014; Morrissey & Cummins, 2016; Van Putten et al., 2016). At the same time, ­chapters  1 to 4 have illustrated that the marine sector is evolving from its traditional natural resource-​based mode of production. The sector is increasingly characterized by high-​tech, service-​based firms that can supply goods and services to a broad range of sectors irrespective of location. These firms are thus more responsive to supply and/​or demand based shocks from the public or private sector than other resource based sectors (Morrissey & O’Donoghue, 2012). However, as the structure of the marine sector changes what impact will this have on regions and coastal areas that once benefitted from traditional marine activities? Van Putten et al. (2016) writing, about small coastal communities in Australia notes that the differences in the structure of the marine sector among coastal communities, suggests that a decline in traditional marine industries will have a disproportional effect on the economic security and social well-​being of certain communities. Van Putten et al. (2016) go on 75

 

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to highlight the need for targeted and location specific governance and policy responses to build resilience among the marine sector in coastal communities. Given the complex dynamics that underlie the marine sector, to answer this question and build appropriate policies, sophisticated tools are required. Chapter 3 demonstrated the usefulness of the IO methodology to examine both the direct and indirect impacts of a sector at the national level. However, the original Leontieff structure envisaged a closed economy with industries responsible for single commodities (Riddington et al. 2006). The reality, of course, is significantly different. At the local level, it is quite conceivable that none of the raw material input to a process is locally produced and equally that consumers in the local industry choose to consume goods that are not produced locally (Riddington et al. 2006). Thus, to understand the impact of an activity at the local level local use tables are required (Riddington et al. 2006). While much of this information is available to construct a local use table, the continued absence of regional import and export statistics to estimate interregional trade flows has restricted the capacity to construct sound regional economic models. Interregional import–​export statistics are important because intermediate inputs purchased from other regions within a given country represent a leakage from the regional economy but are classified as domestic production at the national level. Failure to account for these leakages is likely to lead to certain sectors being seriously over-​valued within some regions (Riddington et al., 2006). Internationally, this lack of regional data has meant that regional modelers in the past have compromised on either the number of regions or the number of sectors included in an analysis (Wittwer & Horridge, 2010). However, if the models have only a handful of sectors, they may behave like a macro model without compositional detail (Wittwer & Horridge, 2010). As the importance of developing the economies of regions becomes more apparent (Moylan, 2011) indirect methods of estimating interregional trade flow patterns has reemerged. A straightforward and inexpensive way of regionalizing a national IO table is to apply a set of employment-​based location quotients (LQs) to estimate trading coefficients (Riddington et al. 2006). Following research by Morrissey (2016) this chapter uses LQs to regionalize the Irish marine IO table developed by Morrissey and O’Donoghue, (2013a) and outlined in ­chapter  3. Regionalizing the Irish marine IO table at the NUTSII level1 will provide policymakers with the direct and indirect impact of the marine economy to the Border, West and Midlands (BMW) and South East (SE) regions of Ireland, as well as indicating key interindustry linkages within the sector and provide policymakers with exploratory 1. NUTS (Nomenclature of Territorial Units) is the spatial classification system used by the European Union.

 



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data on the potential for spatial clusters in the marine sector. This chapter continues as follows: Section 5.2 outlines the theory of economic base theory within a regional context. Section 5.3 offers a comprehensive outline of a number of LQs that may be used to produce a set of regional production multipliers for the marine sector. Section 5.4 presents a case study of the regionalization of marine data using a novel LQ, the FLQ using Irish data. Section 5.5 offers concluding comments on how the LQ method may be used by policymakers and planners to develop a strategy for national and supra-​national marine sectors. 5.2  LOCATION QUOTIENTS In principle, the best way of obtaining the data required to construct a regional input–​output table would be via a well-​designed survey (Flegg et al., 1995; Flegg & Tohmo, 2011). However, such surveys are resource intensive and generally outside the budget of individual research projects (Tohmo, 2004). Thus, given the need to compile regional economic models, indirect methods of estimation have been developed. A straightforward and inexpensive way of regionalizing a national IO table is to apply a set of employment-​based LQs to estimate trading coefficients (Flegg et al., 1995; Flegg & Tohmo, 2011). LQs were first used by Haig (1926) and derived from his work on the economic base analysis. Empirically LQ demonstrate how strongly an industry is represented in a region. It is assumed that on average smaller areas/​ regions are less self-​sufficient than larger regions and almost certainly less self-​sufficient than the country as a whole (Riddington et al., 2006) and the multipliers in these areas will be smaller. However, if there is a development in an area, regardless of size in which there already exists a cluster of suppliers, the multiplier may be larger than the norm (Riddington et al., 2006). In their paper on different methods to regionalise IO tables Riddington et al. (2006) give such an example, where the tourist multiplier for a local area featuring a National Park could well be larger than the country multiplier as a whole if the import penetration in the key sectors is lower. This is particularly interesting in the case of the marine sector where it is well documented that marine activities organically clusters in specific areas. In Finland, a study examining the regional socio-​economic impact of the fisheries industry found that while fishing activity is widely dispersed along the marine resource, the downstream industries, processing and wholesale are highly concentrated in two regions, where the fishing industry itself is most concentrated (Virtanen et al., 2001). In Norway, research using econometric techniques found significant economies of scale across nine marine industries (Midelfart-​Knarvik & Steen, 2002).

 

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Further research in Norway found that regional agglomeration forces within the salmon aquaculture sector had a strong positive effect on productivity in the sector (Tveterås & Battese, 2006). Research in Ireland found that there is the potential for a maritime transport cluster in the Dublin area (Brett & Roe, 2010; Morrissey & O’Donoghue, 2013a, b). Thus, from a policy perspective, there is a strong rationale to produce a regionalized IO table containing data on the marine sector to help identify potential clusters of activities with higher than average returns to the region. For the readers’ ease, the next section provides a quick recap on the IO methodology outlined in ­chapter  3 and then proceeds with an in-​depth explanation of LQs. 5.3  METHODOLOGY The standard mathematical representation of an IO table is:

x = Xe- + f ​



x = A x + f (5.1)



x = (I  –​ A)–​1 f

where matrix X represents the transaction flows between sectors of activities and is the sum of gross outputs, matrix I is an identity matrix, vector x is the sum of gross outputs, vector f represents the part of gross output sold to final demand, and A is a matrix of input coefficients defined as;



A = aij =�

zij xj

(5.2)

where zij is intermediate demand for inputs between sector i and the supply sector j and xj is the final output for sector i. (I  –​ A)–​1 (eq. 5.1) is known as Leontief’s inverse matrix and represents the total direct and indirect outputs in sector i per unit of exogenous final demand, d for sector j (Tohmo, 2004). One of the most important uses of IO models has been the use of the Leontief inverse matrix in the structural analysis of an economy (Tohmo, 2004). One can compute the regional input coefficients, rij, by the corresponding national coefficients, aij, using the formula (Tohmo, 2004):

rij = tij * aij (5.3)

 



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where rij is the regional coefficient; tij is the trading coefficient estimated using LQ; and aij is the national coefficient. Therefore:

r ij = LQij * aij

(5.4)

As noted above, if regional data on imports and exports exist, IO models can be used to study the economic impacts of investments in regions and also the economic structures and interdependency of regions (Tohmo, 2004). However, as this data is usually not available, LQ have been devised. Location Quotients Popularized by Regional Scientists, a simple location quotients (SLQ) may be defined as:



SLQij = �

 REi   NE  i

 TNE    TRE 

(5.5)

where the proportion of regional employment (RE) in each supplying sector i is divided by the corresponding proportion of national employment (NE) in that sector. This is then multiplied by total national employment (TNE) divided by total regional employment (TRE). A SLQi < 1 indicates that sector i is underrepresented in the regional economy and unable to meet all of the needs of regional purchasing sectors for that input. In such cases, the national input coefficient for sector i is scaled downwards by multiplying it by SLQi, therefore creating an allowance for ‘imports’ from other regions. Conversely, where SLQi ≥ 1, the supplying sector is judged to be able to fulfill all requirements of regional purchasing sectors, so no adjustment is made to the national input coefficient. However, it is well noted that the use of SLQs to adjust the national coefficients may produce misleading results (Tohmo, 2004). To explain why such distortions might occur, consider the case where a SLQ is 0.80. As noted above, all input coefficients with a SLQ less than 1 are multiplied by the SLQ to allow for the presumed lesser importance of that sector in the region and the greater reliance on imports to satisfy any increase in regional demand. This presupposes that the discrepancy between the national and regional coefficients is the same, regardless of the downstream sectors to which sector i is selling their output. This presumption cannot be sustained, as it does not take into account of the relative size of the sector providing the inputs and the

 

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sector purchasing them. Moreover, sector i may have specialized in fulfilling the needs of particular sectors and may have no difficulty in satisfying local needs in full. Cross-​industry locution quotients (CILQs) go some way towards overcoming the shortcomings of SLQs. An employment-​based CILQ for sectors i and j may be defined as (Flegg et al., 1995):

CILQij = �

 REi   NE  i

 RE j   NE   ij 

(5.6)

where sector i supplies inputs to j. The logic behind this formula is that, where the supplying sector is relatively small regionally compared to the purchasing sector, the CILQ is < 1, some of the required inputs will have to be met by imports from outside the region (Flegg & Webber, 1995). This means that the national coefficient will need to be adjusted downwards by multiplying it by the CILQ, with a corresponding upward adjustment being made to the relevant import coefficient (Flegg & Webber, 1995; Tohmo, 2004). Similar to the SLQ no adjustment is made if the CILQ is 1. However, research has indicated the poor performance of the SLQ and CILQ in applied settings (Tohmo, 2004). In evaluating these formulae for application, Round (1978) proposed three criteria that an LQ should be met to ensure accurate results. He suggests that a trading coefficient should incorporate three variables in particular: the relative size of the sector i; the relative size of the purchasing sector j; and the relative size of the region. It is evident that the CILQ takes the first two variables explicitly into consideration, yet disregards the third, whereas the SLQ incorporates the first and third, but not the second. Given that neither the SLQ nor the CILQ meets all three criteria, research has indicated that they tend to overstate regional multipliers (Tohmo, 2004). This occurs because these adjustment formulae tend to take insufficient account of interregional trade and therefore understate regional propensities to import (Flegg et al., 1995). Flegg et al. (1995) argue that regional propensities to import are higher than national propensities. The CILQ method of adjustment is an attempt to produce regional input coefficients that are more representative of local conditions than those produced by SLQ adjustment (Tohmo, 2004). According to Flegg et al. (1995) the logic behind CILQ adjustment is that, where the supplying sector is relatively small regionally compared to the purchasing sector, some of the inputs will have to be met by imports from outside the region. National coefficients are adjusted downwards by multiplying them by the

 



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CILQ (Tohmo, 2004). In an effort to address this problem, Flegg et al. (1995) proposed a new employment-​ based location quotient, the FLQ formula, which takes regional size explicitly into account. Flegg et al. (1995) posit an inverse relationship between regional size and the propensity to import from other regions. Brand (1997, p. 792) criticized the initial FLQ formula (see Flegg et al. 1995) on the grounds that the regional scalar λ δ was very insensitive to variations in the relative size of the regional economy to the national economy. Based on this criticism, Flegg and Webber (1997) reformulated the FLQ to become more sensitive to variations in the relative size of the regions. The updated FLQ may be defined as:

FLQij = CILQij × λ* for i = j

(5.7)

where δ



 TRE  λ = log 2 1 +  TNE  *

(5.8)

The regional scalar, λ, has a range from log2 ≈ 0.693 to unity, 0 ≤ δ ≥ 1 (Flegg & Webber, 1997). The inclusion of the regional scalar, λ, ensures that the relative size of the regional purchasing and supplying sectors is taken into account when determining the adjustment for interregional trade, as is the relative size of the region. The inclusion of the parameter δ in the FLQ formula makes it possible to refine the function [log2(1 + TRE/​TNE)] by altering its degree of convexity (Flegg & Webber, 1997); as δ increases, so too does the allowance for interregional imports. δ = 0 represents a special case where FLQij = CILQij (Flegg & Webber, 1997). The FLQ adjustment formula allows for both regional size and the relative size of the purchasing and supplying sectors, and overcomes the tendency of the other LQs to overstate regional multipliers (Tohmo, 2004). In an effort to validate the FLQ formula, Tohmo (2004) carried out an examination of the relative performance of the FLQ, SLQ, and CILQ. Employing the survey-​ based IO table for Finland in 1995 and a corresponding table for one of its regions, Keski-​Pohjanmaa the mean error in estimating the type I sectoral output multipliers was found to be 15.1 percent for the SLQ, 13.1 percent for the CILQ, and −0.3 percent for the FLQ. Thus, Tohmo (2004) found that in the case of Finland, the FLQ was a more accurate method of regionalizing the Finland IO table compared to other LQ methods. In terms of analyzing the results of an LQ analysis, it is generally assumed the higher the location quotient, the more likely it is that this industry has a competitive advantage. Following the economic base theory, an LQ greater

 

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than 1 indicates that a region has proportionately more workers or output than the larger comparison area employed in a specific industry sector. This implies that a region is producing more of a product or service than is consumed by the residents of that region. The excess is available for export outside the region. However, this assumption is not without problems. Crawley, et al. (2013) and Martin and Sunley (2003) both note that there are no commonly agreed or theoretical LQ cut-​off values for defining what a high LQ value is or what LQ value defines a cluster. However, recent research on appropriate LQ cut-​off values to define regional industrial clusters suggested that regions with sectors with an LQ value of 1.25 and above might indicate that a region is specialized in that industrial activity (Crawley et al., 2013). See Crawley, et al. (2013) for a full review of cutoff values for LQ. 5.4  CASE STUDY: IRELAND To examine the regional impact of the marine sector in Ireland, Morrissey (2016) used the LQ methodology outlined above and the Irish IO table containing a disaggregated marine sector (Morrissey and O’Donoghue, 2013). Using the LQs provided by Morrissey (2016) this section examines the economic impact of the marine sector to the Irish Regions. Morrissey (2016) notes that after sensitivity analysis and validation the FLQ was the best performing LQ for the Irish regions. Thus following Morrissey (2016) and using the FLQ, table 5.1 provides the first set of intermediate inputs and production multipliers for the Irish marine sector at the NUTSII regional level. Using the FLQ with an inter-​regional imports parameter δ = 0.8 for the BMW region and the FLQ with an interregional imports parameter δ = 0.3 for the SE region, it was found that the production multipliers were higher in the BMW for fishing and aquaculture, (1.44) oil and gas extraction (1.22), seafood processing (1.81), boat building (1.48) and water construction (1.63). In the SE region, the production multipliers were higher for marine retail (1.55), maritime transport services (1.77), auxiliary transport services (1.35) and marine engineering (0.99). A production multiplier or backward linkage index higher than one implies that the sector has strong backward linkage relative to other sectors in the economy (Morrissey & O’Donoghue, 2013a). An industry with higher production multipliers than other industries indicates that expansion of its production is more beneficial to the economy in terms of inducing productive activities. Thus, regional backward linkages serve as indicators for the performance of a sector from the perspective of the regional economy (Baaijens et al., 1998). Regional multiplier or backward linkage analysis is a useful method for regional policy impact assessment (Baaijens et al., 1998).

 



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Table 5.1  Total Intermediate Inputs and Production Multipliers for the Marine Economy in the Nutsii Bmw and Se Regions in Ireland Sector Fishing & Aquaculture Oil & Gas Extraction Seafood Processing Boat Building Water Construction Marine Retail Maritime transport services Auxiliary Maritime Transport Services Marine Engineering WBA

BMW Coefficient

BMW FLQ 0.8

0.47 0.3 0.75 0.49 0.61 0.43 0.59 0.28

1.44 1.22 1.81 1.48 1.63 0.82 1.33 0.87

0.28 0.002 0.01 0.002 0.002 0.43 0.59 0.28

1.35 0.98 0.99 0.98 0.98 1.55 1.77 1.35

0.46 0.43

0.84 1.39

0.01 0.1

0.99 1.11

SE Coefficient

SE FLQ 0.3

Examining the BMW region first, table 5.1 indicates that the natural resource based marine sectors (Morrissey et al., 2011) including fishing and aquaculture, seafood processing, oil and gas extraction have high backward linkages. Thus expansion of these sectors would be beneficial to the BMW region. Looking at the seafood industry specifically (defined as fishing and aquaculture and seafood processing), one can see that for every €1 produced within the fishing and aquaculture and seafood processing sectors, €0.44 and €0.81 respectively, €1.25 for the seafood sector as a whole, is backward linked to direct and indirect upstream suppliers. Overall, the average backward linkage for the marine sector in the BMW region is 128. This indicates that the sectors in the BMW marine economy had high (greater than one) backward linkage effects within the region. Thus, although the BMW economy as a whole relies on interregional imports within the national economy, the marine sector is a strong self-​reliant production oriented sector for the region. Extending the analysis for the BMW region, using the SEMRU marine company database (Morrissey, 2010) to examine the location and percentage of seafood based activity in the BMW region, it was found that 23 percent of seafood based enterprises, 46 percent of seafood based gross value added (GVA) and 24 percent of seafood-​based employment was based in county Donegal (nested in the BMW region) alone. With regard to the SE region, table 5.1 indicates the service-​based marine sectors (Morrissey et al., 2011) including maritime transportation, services to maritime transportation and marine retail have high backward linkages and thus expansion of these sectors would be beneficial to the SE region. Looking at the maritime transportation sector specifically, one can see that for every €1 produced within the maritime transportation and maritime

 

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transportation services sector, €0.77 and €0.35 respectively is backward linked to its direct and indirect upstream suppliers. This result is similar to findings by Morrissey and O’Donoghue (2013b) using the IO method and Brett and Roe (2010) using the RAND method found that the SE region of Ireland is a maritime transportation hub and has the potential to become a maritime transportation cluster. Overall, the average backward linkage for the marine sector in the BMW region is 121. This indicates that the sectors in the SE marine economy had high (greater than one) backward linkage effects within the region. Thus, the marine sector is a strong, self-​reliant, production oriented sector for the SE region. Comparing the marine sector across the BMW and SE regions, the backward linkages for the marine sector are higher in the BMW region (128) compared to the SE region (121). This indicates that the marine sector is slightly more important in the production process of the BMW region compared to SE region. This finding confirms the study by Morrissey and O’Donoghue (2012) that, in relative terms, the marine sector is of greater importance to the North West region of Ireland (which is nested within the BMW region) compared to other Irish regions. Discussion of the Irish Case Study Using currently available data on the marine sector and a relatively novel LQ, the FLQ (Flegg & Tohmo, 1997), this Case Study found that the marine sector has a greater role in the overall production process of the BMW region compared to the SE region of Ireland. This result confirms previous research that found that the marine sector as a whole is relatively more important in the Border region of Ireland (which is nested in the BMW region) than the other Irish regions (Morrissey & O’Donoghue, 2012). Using the FLQ method to examine the marine sector for the two NUTSII regions in Ireland, it was found that the seafood industry is an important industrial sector within the BMW region. Similar to previous analyses of the Irish maritime transportation sector (Morrissey & O’Donoghue, 2013b; Brett & Roe, 2010) it was found that the maritime transportation sector has high production effects in the SE region. So what does this mean from a policy perspective? Reflecting the insights of international research (Porter, 1990) cluster theory has become the focal point for many new industrial policy initiatives (Dolereux & Shearmur, 2009). Cluster theory argues that internationally competitive industries usually occur in the form of specialized clusters of indigenous, “home-​ based” industries (Porter, 1990). These industries are linked together through vertical relationships (buyers–​ suppliers) or horizontal relationships (common customers, technology, skills, distribution channels, etc.). However, while both academic research (Chang, 2011) and

 



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policy-​makers (Clancy et al., 2001) have applied the concept of cluster to a diverse range of topics, little research has been done to assess the potential for developing clusters within the marine based economy. The one notable exception is the maritime transportation sector, where international (Chang, 2011; Dolereux & Shearmur 2009; De Langen & Visser 2005; Wang, 2008) and Irish (Brett & Roe, 2010; Morrissey & O’Donoghue, 2013b) research has focused on the potential of developing maritime transportation clusters. A further aim of this paper was to extend previous research on the potential of marine based clusters, which have to date, tended to focus on the maritime transportation sector. As outlined in ­chapter  4, the natural resource sector is usually posited as a sector with poor agglomeration economies (Gruber & Soci, 2010; Morrissey & O’Donoghue, 2012), which does not benefit from proximity to suppliers, knowledge spillovers or a joint labor pool. However, the analysis provided by Morrissey (2016) and reported above, indicates that the seafood sector, fishing, aquaculture, and seafood processing, has strong backward linkages within the BMW economy. The regional production multipliers derived from the FLQ analysis can be interpreted as the direct and indirect benefits, which would ensue from future seafood based development projects in the BMW region. For every €1 of investment in the seafood sector in the BMW region, €1.25 is backward linked to its direct and indirect upstream suppliers. Chapter 4 also documented that in terms of GVA, employment and enterprises, County Donegal had the highest spatial cluster of seafood based activity in Ireland. Thus, the regional economic theory would suggest that stimulating investment in the seafood sector would positively affect upstream industries in the less prosperous BMW region, particularly in County Donegal. This data may now be used to better inform future marine planning and investment decisions for the Irish regions at both the national and EU level (Koehn et al., 2013; Jin et al., 2013). 5.5  CONCLUSIONS One of the main objectives of this book is to increase the number of tools; data and economic indicators that may be used to formulate policy for the marine economy. This chapter outlined the potential to use LQ to regionalize an augmented IO table. Presenting a Case Study of the Irish marine economy (Morrissey, 2016) found that LQ are a low-​cost, effective tool to examine the impact of the marine sector to regional economies. Methodologically, the Case Study on the Irish marine sector presented in section 5.3, demonstrated that similar to Tomho (2004) that the FLQ method of regionalizing the Irish IO table was superior to the traditional SLQ and CILQ methods. The LQ approach to regionalizing national IO tables outlined in this chapter

 

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provides policymakers with more insight into the current regional structure and the level of potential regional “gains” or “leakages” if public investment was to occur in a particular sector. This insight would allow policymakers to strategically locate new investment in specific sectors, based on empirical evidence of regional advantage. Finally, this chapter provides a template for international research interested in regionalizing IO tables both in the marine sector and the wider economy. BIBLIOGRAPHY Baaijens, R., Nijkamp, P., van Montfort, K. (1998). Explanatory meta-​analysis for the comparison and transfer of regional tourist income multipliers. Regional Studies 32(9):  839–​849 Benito, G. R., Berger, E., De La Forest, M., Shum, J. (2003). A cluster analysis of the maritime sector in Norway. International Journal of Transport Management 1: 205–​206. Brand, S. (1997). On the appropriate use of location quotients in generating regional input–​output tables: A comment. Regional Studies 31: 791–​794. Brett, V., Roe, M. (2010). The potential for the clustering of the maritime transport sector in the Greater Dublin Region. Maritime Policy & Management 37(1): 1–​16. Chang, Y. (2011). Maritime clusters: What can be learnt from the South West of England. Ocean & Coastal Management 54: 488–​494. Clancy, P., O’Malley, E., O’Connell, L., Van Egeraat, C. (2001). Industry clusters in Ireland: an application of porter’s model of national competitive advantage to three sectors. European Planning Studies 9(1): 7–​28. Colgan, S. (2013). The ocean economy of the United States: Measurement, distribution, & trends. Ocean and Coastal Management 71: 334–​343. Crawley, A., Beynon, M., Munday, M. (2013). Making location quotients more relevant as a policy aid in regional spatial analysis. Urban Studies 50: 1854–​1869. Cummins, V., Kopke, K. (2011). Editorial: reflections from Ireland. Marine Policy 35(6):  737–​738. Department of Agriculture, Fisheries and Food (2010). Food Harvest 2020: A vision for Irish Agri-​food and Fisheries. Department of Agriculture, Fisheries and Food, Dublin. Doloreux, D., Shearmur, R. (2009). Maritime clusters in diverse regional contexts: the case of Canada. Marine Policy 33: 520–​527. De Langen, P. W. (2002). Clustering and performance: the case of maritime clustering in The Netherlands. Maritime Policy & Management 29(3): 209–​221. De Langen, P. W., Visser, E. J. (2005). Collective action regimes in seaport clusters: the case of the lower Mississippi port cluster. Journal of Transport Geography 13: 173–​186. Doloreux, D. (2004). Regional innovation systems in Canada: a comparative study. Regional Studies 38: 479–​492.

 



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Egeraat, C. (2007). The Scale and Scope of Process R&D in the Irish Pharmaceutical Industry NIRSA Working Paper No. 32, NUI, Maynooth. Eggert, H., Tveterås, R. (2013). Productivity development in Icelandic, Norwegian and Swedish Fisheries. Applied Economics 45(6): 709–​720. Flegg, A., Tohmo, T. (2013). Regional Input–​output tables and the FLQ formula: a case study of Finland. Regional Studies 47(5): 703–​721. Flegg, A. T., Webber, C. D. (1997). On the appropriate use of location quotients in generating regional input–​output tables: reply. Regional Studies 31: 795–​805. Flegg, A. T., Webber, C. D., Elliott, M. V. (1995). On the appropriate use of location quotients in generating regional input–​output tables. Regional Studies 29: 547–​561. Gertler, M. S., Wolfe, D. A. (2006). Spaces of Knowledge Flows. Routledge, London. Giblin, M., Ryan, P. (2012). Tight clusters or loose networks? The critical role of inward foreign direct investment in cluster creation. Regional Studies 46(2):  245–​258. Gruber, S., Soci, A. (2010). Agglomeration, agriculture, and the perspective of the periphery. Spatial Economic Analysis 5(1): 43–​72. Irish Marine Resource and Energy Cluster (2011). Irish Marine Resource and Energy Cluster Strategy 2011–​ 2016, http://​www.imerc.ie/​pages/​IMERC_​Strategy_​2011-​ 2016.pdf. Jin, D., Hoagland, P., Wikgren, B. (2013). An empirical analysis of the economic value of ocean space associated with commercial fishing. Marine Policy 42: 74–​84. Haig, R. M. (1926). Toward an understanding of the metropolis. Quarterly Journal of Economics 40: 421–​433. Kildow, J. T., McIlgorm, A. (2010). The importance of estimating the contribution of the oceans to national economies. Marine Policy 34: 367–​374. Koehn, Z., Reineman, J. K. (2013). Progress and promise in spatial human dimensions research for ecosystem-​based ocean planning. Marine Policy 42: 31–​38. Kwak, S. J., Yoo, S. H., Chang, J. I. (2005). The role of the maritime industry in the Korean national economy: an input–​output analysis. Marine Policy 29(3): 371–​83. MacFeely, S., Moloney, R., Kenneally, M. (2011). A Study of the NUTS 2 Administrative Regions using Input-​Output Analysis, University College Cork, Cork. MacLoughlin, J. (2010). Troubled Waters: A Social and Cultural History of Ireland’s Sea Fisheries. Four Courts Press, Dublin. Martin, R., Sudley, P. (2003). Deconstructing clusters: Chaotic concept or policy panacea. Journal of Economic Geography, 3: 5–​35. Midelfart-​Knarvik, K. H., Steen, M. (2002). Delocation and European integration: is structural spending justified? Economic Policy 17: 321–​360 Morrissey, K. (2016). A location quotient approach to producing regional production multipliers for the Irish economy. Papers in Regional Science, 95(3), 491–​506. Morrissey, K., Cummins, V. (2016). Measuring relatedness in a multisectoral cluster: An input–​output approach. European Planning Studies, 24(4): 629–​644. Morrissey, K., O’Donoghue, C., Hynes, S. (2011). Quantifying the value of multi-​ sectoral marine commercial activity in Ireland. Marine Policy 35: 721–​727. Morrissey, K., O’Donoghue, C. (2012). The Irish marine economy and regional development. Marine Policy 36: 358–​364.

 

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Morrissey, K., O’Donoghue, C. (2013a). The role of the marine sector in the Irish national economy: An input–​output analysis. Marine Policy 37: 230–​238. Morrissey, K., O’Donoghue, C. (2013b). The potential for an Irish maritime transportation cluster. Ocean & Coastal Management 71: 305–​313. Moylan, K. (2011). Irish Regional Policy, In Search of Coherence, Issues in Public Administration. Institute of Public Administration, Dublin. Othman, M., Bruse, G., Hamid, S. (2011). The strength of Malaysian maritime cluster: The development of maritime policy. Ocean and Coastal Management 54:  557–​568. Porter, M. E. (1990). The Competitive Advantage of Nations. MacMillan, London. Round, J. I. (1978). An interregional input–​output approach to the evaluation of non-​ survey methods. Journal of Regional Science 18: 179–​194. Riddington, G., Gibson, H., Anderson, J. (2006). Comparison of gravity model, survey and location quotient-​based local area tables and multipliers. Regional Studies 40(9):  1069–​1081. Sigfusson, T., Arnason, R., Morrissey, K. (2013). The economic importance of the Icelandic Fisheries Cluster -​Understanding the role of fisheries in a small economy. Marine Policy 39: 154–​161. Shinohara, M. (2010). Maritime cluster of Japan: Implications for the cluster formation policies. Maritime Policy & Management: The Flagship Journal of International Shipping and Port Research 37: 377–​399. Stojanovic, T. A., Farmer, C. (2013). The development of world oceans & coasts and concepts of sustainability. Marine Policy 42: 157–​165. Surís-​Regueiro, J. C., Garza-​Gil, M. D., & Varela-​Lafuente, M. M. (2014). Socio-​ economic quantification of fishing in a European urban area: The case of Vigo. Marine Policy, 43: 347–​358. Tohmo, T. (2004). New developments in the use of location quotients to estimate regional input–​output coefficients and multipliers Regional Studies 38(1): 43–​54. Tveteras, R., Battese, G. E. (2006). Agglomeration externalities, productivity, and technical inefficiency. Journal of Regional Science 46(4): 605–​625. van Putten, I., Cvitanovic, C., & Fulton, E.A. (2016). A changing marine sector in Australian coastal communities: An analysis of inter and intra sectoral industry connections and employment. Ocean & Coastal Management, 131: 1–​12. Virtanen, J., Ahvonen, A., Honkane, A. (2001). Regional socio-​economic importance of fisheries in Finland. Fisheries Management and Ecology 8: 393–​403. Wang, C. X. (2008). Optimization of hub-​and-​spoke two-​stage logistics network in regional port cluster. Systems Engineering: Theory & Practice 28: 152–​158. Wittwer, G. & Horridge, M. (2010). Bringing Regional Detail to a CGE Model using Census Data, Spatial Economic Analysis, 5(2): 229–​255.

 

Chapter Six

Regional Development and Marine Clusters

6.1  INTRODUCTION The policy focus on cluster development in the marine sector reflects the insights of research in the early nineties by Porter (1990). As a starting point, Porter argued that for national competitive advantage to occur, it is not sufficient to have a number of successful but unconnected industries. Porter pointed out that in a globalized economy, competitive advantages are increasingly associated with local, territorially embedded economies (Lazzeretti & Capone, 2010) that are connected to each other through various supporting conditions. Porter’s work (1990) continued by illustrating that industries could create competitive advantage by complementing (vertical linkages) and co-​operating (horizontal linkages) with each other within a common value chain. According to Porter, competitive advantage among these firms arises as result of the Marshallian idea that geographic proximity creates the type of collaborations, knowledge spillovers, and positive externalities that companies can use and exploit (Gruber & Soci, 2010; Eriksson, 2011). These externalities are based on the presence of qualified labor, production inputs (e.g., support services), and benefits stemming from industrial technological advancement (Gruber & Soci, 2010; Eriksson, 2011; Titze et al., 2011). Porter continued by noting that it is because of the benefits of these externalities that many otherwise “footloose” and mobile firms form high spatial concentrations or “spatial cliques” (Gruber & Soci, 2010; Poon et al., 2013). Labor externalities arise when there is a concentration of a certain industry in a particular territory that allows the formation of a specialized labor pool. The presence of qualified labor in the territory not only reduces the costs of searching and recruiting the workforce, but it also makes available rare competencies that would be more expensive anywhere else. Lazzeretti and 89

 

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Capone (2010) further note that the concentration of specialized workforce fosters the institution of specific training courses and vocational training schools, which on their turn increase the competencies of local workers. Externalities in production inputs arise due to the geographical proximity of suppliers, so that transportation as well as supervision and transaction costs are reduced, and face-​to-​face contacts intensified. Finally, externalities arising from knowledge spillovers arise because colocation of companies within an industry allows for the faster and cheaper spread of knowledge due to geographical proximity, more frequent contacts and the building of trust among operators. Based on these externalities Porter (1990) identified four determinants of competitive advantage, presented in a diamond diagram, which encourages cluster formation. These determinants include; • Factor conditions (e.g., employee skill set), • Demand conditions (predominately indigenous), • Related and supporting industries (to develop a skilled employee base or technical knowledge) and • Firm strategy, structure and rivalry (through knowledge transfer). The conditions that bring about industry clustering grow directly out of these four determinants. Each “point” on the diamond affects the business environment by: • Reducing transaction costs, which makes it easier for the companies to specialize in a specific part of the value chain; • Maximizes the utilization of complementarities in the input of resources, which may: a) Create scale economies in production; and b) Create chances to reach critical mass of demand necessary for the production of particular goods or services. • Substitution of resources and enhancement of competition locally; • Creates better access to skilled labor; • Sharing of knowledge and information, and learning through networking; • Develop leader firms; and • Development of coordinating institutions. The diamond model also includes two residual influences, government and chance events. Traditionally, a policy provision and top down support was seen as a governmental role within cluster development. While cooperation between linked companies may occur spontaneously, policies to foster the development of groupings of companies or industries should also focus on

 



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support for cooperation and alliances between relevant companies. According to Porter, the role of (local) governments in the diamond framework is to foster every single part of the diamond and strengthen the arrows that join them by: • Encouraging companies to raise their performance, for example by enforcing strict product standards. • Stimulating early demand for advanced products. • Focusing on specialized factor creation. • Stimulating local rivalry by limiting direct cooperation and enforcing antitrust regulations. From a partner’s perspective, cluster development means a shift in focus from the firm to productive systems and understanding competitiveness as a collective result rather than the outcome of individual processes (Fingleton et al., 2005). Within this context, although cooperation between linked companies may occur spontaneously, policy provision and support for clusters is traditionally seen as a governmental role. However, recent research has demonstrated that rather than a top down governmental approach, other types of institutional interventions, both public and private, can function to facilitate cooperative structures and the development of infrastructure for regulating cooperation (Cooke & Morgan, 1998; Staber, 1996). Steps to support cooperation and alliances can be taken by regional and local authorities as well as national development agencies and networks initiated by both public and private organizations. Finally, Porter (1990) notes the importance of chance events. Chance events are events occur outside of the control of the firm are important because they create discontinuities in which some companies gain competitive positions and others lose (­figure  6.1). The basic idea behind the Porter’s Diamond is that companies’ actions are dependent on the environment in which they operate (Benito et al., 2003). Companies in the same or related industries develop strong relations and interdependencies over time and the simultaneous existence of competition and cooperation leads to a competitive environment. In turn, competitive environments drive innovativeness and promote competence building through rivalry and the exchange of knowledge. This in turn helps develop a competitive edge for each company. Key to this systemic growth is the exchange and flow of information about needs, techniques, and technology. Thus, it is within this context that the importance of geographic concentration emerges, since proximity greatly facilitates the flow of this information central to the capability to innovate and to upgrade competitive advantage. Producing an extensive biblometric review of industrial clusters, Hervas-​ Oliver (2015) conclude that although the literature has evolved across

 

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Figure 6.1.  Porters Diamond Model.

different subdisciplines, each discipline emphasizes the influence of colocation on cluster performance. Even though each of the different factors is important in order to create well-​functioning industrial clusters, it is imperative that they work together rather than in isolation. It is the self-​reinforcing interplay between the factors, not just co-​location alone, that constitutes the basis for a strong cluster within an industry (Porter, 2000; Benito et al., 2003). According to Porter (2003) this is the essential difference between a cluster and the clustering of industries. Within this context, Porter (2000, p. 15) defines a cluster as a “geographic concentration of interconnected companies, specialized suppliers, service provides, firm in related industries, and associated institutions (e.g., universities, standards agencies, trade associations) in a particular field that compete but also cooperate.” However, despite being highly influential particularly in the policy arena, Porter’s original contributions have been heavily criticized for lack of preciseness according to the sectorial and spatial characteristics of a cluster (Bathelt, 2005; Martin & Sunley, 2003). As such, other definitional contributions include Malmberg and Power (2005, p. 57) who have pointed towards some generic criteria for cluster formation: “There should be a spatial agglomeration of similar and related economic activity; these activities should be interlinked by relations and interactions of local collaboration and competition; there should be some form of self-​awareness among the cluster participants and some joint policy action (we

 



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are a cluster and we are determined to develop together).” In a similar fashion, Ketels (2003) defines industrial clusters as “groups of companies and institutions co-​located in a specific geographic region and linked by interdependencies in providing a related group of products and/​or services” (Ketels, 2003). Regardless of definitional nuances, industrial clusters are increasingly seen as a policy that can improve the business environment, capabilities, and performance of local firms in targeted industries (Doloreux & Shearmur, 2009). It is within this context that many maritime countries and regions have moved in the direction of having their marine industries represented by cluster organizations (Salvador, 2014). This is particularly true for the maritime transportation (maritime) sector, where the concept of clusters has received significant attention both in the academic and policy literature (Salvador, 2014). Indeed, presenting a Global Maritime Benchmarking study, Wijnolst et al. (2003) argue that Europe should organize itself into a “vast continental maritime cluster.” The reminder of this chapter focuses on the development of maritime clusters. The ability to examine the economic impact of a maritime cluster is further enabled by data availability on the maritime sector. Although data limitations have meant that certain sectors of the marine sector are difficult to empirically analyze, data on maritime transportation and port activity are provided within most country’s national accounts. As such a number of studies have used IO analysis to examine a number of factors of relevance to the maritime sector. A case study using Location Quotients and IO analysis is presented as a method to examine the potential for an Irish maritime cluster around Dublin port. 6.2  CLUSTERS AND THE MARINE ECONOMY As outline in ­chapter  2, the maritime sector is multisectoral and includes industries both directly (e.g., fishing and oil and gas extraction sectors) and indirectly (e.g., blue biotechnology and offshore logistic computing systems) related to the marine resource (Colgan, 2013; Kildow & McIlgorm, 2010; Morrissey et al., 2011). As such, in theory a maritime cluster has the potential to represent any number or combination of maritime subsectors outlined in ­chapter  2. However, to date, the majority of maritime clusters have been based on exploiting agglomeration economies between firms belonging to the same maritime subsector, with a particular focus as we will see later in this section on the maritime transportation (maritime) sector. The focus on maritime clusters is based on the emphasis the cluster literature has placed on the co-​location of companies as the most important mechanism to create a successful cluster. Industries differ to the extent to which they can choose locations (Ketels, 2003). Many industries are tied to their location by the need

 

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to be close to their physical features, particularly natural resource based sectors such as mining and agriculture. Mapping the location of marine based firms in Ireland, Morrissey and O’Donoghue (2012) argue that the marine sector is not necessarily tied to coastal locations. In Ireland companies producing marine based goods and services are based throughout the eight Irish NUTS regions (Morrissey & O’Donoghue, 2012). However, given the infrastructural necessity of ports for shipping activity and downstream activities such as cargo handling, storage, and so on (­figure  6.2) to be located close to ports, the shipping sector and its downstream sectors are increasingly seen as readymade clusters (Zhang & Lam, 2013). Given the focus on cluster theory and agglomerations in industrial policy and the natural “cluster” of maritime activities around ports a number of papers have examined the potential for and/​or impact of maritime clusters. Maritime Clusters Building on Porter, Doloreux and Shearmur (2009) define a maritime cluster as a geographic concentration of firms in maritime sectors, of research

Figure 6.2.  Industries that Provide Goods and Services to Maritime Shipping.

 



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and education organizations which are active in related fields, and of public support mechanisms operated by the government and regional stakeholders. Like all industrial clusters, these firms and institutions interact, exchange knowledge, share information, cooperate and compete, leading to a common vision of growth and innovation. Research on the Norwegian maritime transportation sector found that it complies with most of the characteristics of a strong industrial cluster including strong intersectoral linkages, sectoral diversity, and competitive rivalry (Benito et al., 2003). Research on the Dutch maritime sector (De Langen, 2002) found that clustering among different maritime industries could be observed at different levels. For example, at lower levels of aggregation it was found that ports could be regarded as the core of two large clusters. Further, research in Norway using econometric analysis examined potential linkages between the service-​ oriented shipping sectors and the manufacturing-​ oriented ship industry sectors (Knarvik & Steen, 1999). Their analysis revealed that significant economies of scale exist in the maritime industry. Such economies of scale were mainly found within subclusters rather than between subclusters, suggesting that the predominantly downstream-​ oriented shipping sector and the mainly upstream oriented ship industry sectors largely behaved as two self-​reinforcing but independent sub-​clusters. From this analysis, it was concluded that the strength of intersectoral linkages ensures that the regional, coastal location of the marine companies will be self-​reinforcing and specific regional policy must be devised for the sector to evolve further (Knarvik & Steen, 1999). Focusing on the maritime sector in the South West of England, Chang (2011) found that the maritime transportation based businesses make up 45 percent and 57 percent of the region’s businesses and labor force, respectively. Given the relative importance of the sector to the region, Chang (2011) found that establishing a maritime cluster in the region would optimize the contribution each port could make. Chang (2011) further notes that since subcontractors produce most of the value-​added products in maritime industries, clusters can, therefore, offer subcontractors access to information and valuable knowledge that they cannot otherwise afford. Based on these findings Chang (2011) states that “maritime clusters” can be defined as a network of firm, research, development and innovation units and training organizations, sometimes supported by national or local authorities, which cooperate with the aim of technology innovation and of increasing maritime industry’s performance. Research in Ireland using the Delphi method found that the greater Dublin area currently displays evidence of firm agglomeration across the maritime transportation sector in and around Dublin port (Brett & Roe, 2010).

 

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Examining the strength and sustainability of the Japanese maritime cluster, Shinohara (2010) found that to maintain a maritime cluster there were a number of key requirements. These include: 1. Strong government support for incubating each industry at the initial stage of cluster formation; 2. business networking, especially long-​term relationship between firms and support from financial institutions, is essential; and 3. human resource management based on the long-​term co-​working spirit is vital. Examining the Portuguese maritime cluster (Salvador et al., 2016) found that intermediate linkages between the Portuguese maritime sectors were weak, especially when compared with other EU maritime clusters. However, three subsectors had high multiplier values (both types 1 and 2); “maritime transport,” “port”, and “recreational boating and marinas.” Based on these findings, Salvador et al. (2016) propose that priority should be given to these three sectors. Finally, assessing the success of three maritime clusters in Canada, St. John’s, Newfoundland; Victoria, Vancouver and St. Lawrence, Quebec, Doloreux and Shearmur (2009) found that whilst St. John’s and to a lesser extent Victoria showed signs of becoming self-​sustaining, globally oriented clusters. Research has therefore indicated the positive effect regional agglomeration around maritime transport networks and other downstream, interlinked industries may have on the sector as a whole. 6.3  CASE STUDY: AN IRISH MARITIME CLUSTER Within the EU, the move to a single market and the increased rate of globalization has led to a recognition that not all industrial sectors would benefit from trade liberalization. The difficulty in maintaining traditions and safeguarding employment is particularly evident in the shipping sector (Alderton & Winchester, 2002). Central to world trade, transporting approximately ninety percent of internationally traded produce (Trant & Liddane, 2010) the shipping industry has undergone dramatic changes in the last two decades. Acknowledging the need for direct measures to halt the decline in the shipping industry and to prevent the migration of the industry to alternative jurisdictions the EU has introduced a range of policies aimed at stimulating the sector. The core of these policies is the development of maritime transportation cluster (Wijnolst et al., 2003). However, while there is clear evidence that clustering can produce economic development within a sector, research has documented concerns that the current emphasis on cluster formation is rarely supported by

 



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empirical evidence that (a) indicates why a particular sector should be a focus of resources, and (b) how far their promotion might be linked to regional/​ national growth prospects and competitiveness. Thus, using IO analysis, this chapter seeks to empirically examine the potential for cluster formation within the maritime transportation sector, based on the strength of its linkages with its support services and with the wider Irish economy. Location Quotients To begin, an important tool in examining the spatial concentration of firms is location quotients (De Langen, 2004). These quotients show to what extent a region is specialized in specific activities (Othman et al., 2011). Assuming that the cluster as a whole is relatively important in the region, the higher the location quotient, the more likely it is that this industry is a part of the cluster. Location quotients (LQs) may be defined as:

LQi = (REi/​NEi) * (TNE/​TRE) (6.1)

where the proportion of regional employment (proxy for economic activity) in each supplying sector i is divided by the corresponding proportion of national employment in that sector. A LQ > 1 indicates there is a relatively high concentration of the activity in region i compared to the national level. An LQ = 1 indicates that activity in the region is in accordance with its share of the base and an LQ < 1 indicates there is less concentration of the activity in a specific region. Using regional data on the maritime transportation sector (Morrissey & O’Donoghue, 2012) and applying the simple location quotient equation (eq. 1), it was found that the South Eastern NUTII region has an LQ of 1.15 and the Border Midlands and West region had an LQ of 0.44. The South Eastern region consists of both the Port of Dublin and the Port of Cork, the two largest ports in Ireland, respectively. Dividing the number of maritime transportation firms active in each county by the overall number of firms in the sector, it was found that Dublin had a higher percentage maritime transportation companies (38 percent of the overall population) compared to Cork (18 percent). This result is consistent with recent research that indicates that there is a spatial agglomeration of maritime transportation industries in Dublin port (Brett & Roe, 2011). Linkages Within the Irish Maritime Sector An important dimension of industry and cluster development is the nature or strength of buyer–​supplier link (Midmore et al., 2006). Sectors do not exist in a vacuum; rather they rely on other sectors for inputs (backward linkages)

 

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into their production process, while simultaneously selling their output to sectors (forward linkages) to generate profit. Backward linkage effects are strongly induced by industries with high intermediate input coefficients, such as manufacturing industries. Symmetrically, strong forward linkages are generally induced by the primary and material industries, whose outputs are used by other industries as intermediate goods (Kwak et al., 2005). The intensity of intersectoral linkages between related industry groups has been highlighted as a key determinant of the technical and competitive progress of an economy (Driffield et al., 2002). While strong linkages among firms is seen as a key indication of cluster potential (Morrissey & O’Donoghue, 2013a; Morrissey et al., 2013b). As such, the identification of sectors that display strong linkages is believed to be a useful planning tool for stimulating overall economic growth. Considering the complexity of intersectoral linkages, it would be an enormous task to trace and measure an entire sector’s direct and indirect backward and forward relations to other sectors. However, as outlined in ­chapter  2, IO models may be used to trace the entire backward and/​or forward linkages within a sector. Chapter 2 outlined the marine sector disaggregated IO table created by Morrissey & O’Donoghue, 2013a). The backward and forward linkages derived from ­chapter  2 will be used to establish the potential for a maritime cluster in Ireland based on the strength of the sectors linkages within the maritime sector more broadly and across the national economy. Linkages Within the Maritime Transportation Sector From table 6.1 one can see that the maritime transportation has the (joint) second highest backward linkage (1.09). This implies that for every €1 produced within the water transportation sector, €0.09 is backward linked to its direct and indirect upstream suppliers. Four cents of this €0.09 belongs to the water transportation sectors’ direct suppliers and €0.05 belongs to its indirect suppliers (e.g., the suppliers of its direct suppliers). Furthermore, as noted a backward linkage greater than one implies that the sector is an important input suppliers to other sectors. Placing the maritime sector within the context of the wider economy, the average backward linkage for the Irish economy was 0.58. This indicates that the sectors in the wider Irish economy had low (less than one) backward linkage effects. This result is not surprising given that Ireland is a small open economy and many of its inputs into the process of production are imported from outside the country. Indeed, further analysis of the Irish IO table found that on average imports for each of the sixty-​two sectors as a percentage of inputs were 60 percent across the wider economy. In contrast, the ratio of imports to exports water transportation sector is 16 percent. This further indicates the key linkages between the maritime

 



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Table 6.1  Backward Linkages in the Irish Economy and the Irish Marine Sector Top 10 sectors with the strongest backward linkages Other mining and quarrying Seafood Processing Research and development services Maritime Transportation Sewage and refuse disposal services Water Construction Post and telecommunication services Forestry Construction work Membership organization services

134 126 109 109 107 106 103 96 94 90

Table 6.2  Sectors with Backward Linkages to the Maritime Transport Sector Sectors with Backward Linkages to the Maritime Transport Sector Water transport services Auxiliary transport services and travel agencies Computer and related services Auxiliary Transport Marine Financial intermediation services Post and telecommunication services Insurance and pension services Other business services Petroleum and other manufacturing products Motor fuel and vehicle trade and repair Highest Linkage Score in the Overall Irish I–​O table –​134 Average Linkage Score –​31 Average Linkage Rank –​58

47 18 8 4 4 3 3 3 2 2

transportation and the broader marine sector and indigenous companies within the Irish economy. Table 6.2 continues the analysis by presenting the sectors with which the maritime transportation sector has the highest backward linkages. From table 6.2, one can see that the sector has high backward linkages (excluding itself) with auxiliary transportation services, computer and related services, maritime specific auxiliary transportation services (these services include berthing, liner and port services and facilities), financial intermediation services, post and telecommunications, insurance and pension, petroleum and other manufacturing products and motor fuel and vehicle trade and repair. These results indicate that for the maritime transportation sector the most important input suppliers are within the professional service sector. The Irish

 

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government has developed a number of policy supports, notably the Irish tonnage tax system, to encourage the growth and development of a high tech, professional service led, maritime cluster (Murphy & Richard, 2008). Forward Linkages Table 6.3 presents (a) the sectors that are most strongly forward linked within the Irish economy and (b) the forward linked for each of the ten marine sectors. As indicated above, a sector is forward linked to other sectors through its direct and indirect sales to them. Overall, the average backward linkage for the Irish economy was 0.62. From table 6.3 one can see that the maritime transportation sector has the seventh highest forward linkage within the Irish economy with a value of 1.20. This implies that every €1 produced by the maritime transportation sector is forward linked to €0.20 to the production of the sectors direct and indirect downstream demanders. In detail, for €1 of the production of water transportation services, €0.49 is sold directly for final consumption, including €0.08 for local consumption and €0.41 for exports. The rest €0.20, are bought by the water transportation sectors downstream demanders. The high forward linkage of the maritime transportation sector reflects the fact that the service provided by the sector is primarily consumed as part of the intermediate production process by consuming industries. Examining the sectors with which the maritime transportation sector has high forward linkages (table 6.4), one can see that (excluding itself) the sector has high forward linkages with wholesale trade (0.2), post and telecommunications (0.1), construction work (0.1), auxiliary transportation services (0.06), hotel and restaurant services (0.03), fishing (0.02), food and beverages (0.02), motor fuel and vehicle trade and repair (0.02) and services auxiliary to financial intermediation (0.02). Placing the high forward linkage demonstrated by

Table 6.3  Forward Linkages in the Irish Economy Top 10 sectors with the strongest forward linkages Forestry Other mining and quarrying Recycling Other non-​metallic mineral products Post and telecommunication services Electricity and gas Maritime transportation services Wood and wood products Services auxiliary to financial intermediation Fabricated metal products

199 185 176 148 139 137 120 116 114 111

 



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Table 6.4  Sectors with Forward Linkages to the Maritime Transport Sector Sectors with forward linkages to the maritime transport sector Water transport services Wholesale trade Post and telecommunication services Construction work Auxiliary transport services and travel agencies Hotel and restaurant services Fishing Food and beverages Motor fuel and vehicle trade and repair Services auxiliary to financial intermediation Highest linkage within the Disaggregated Irish I–​O table –​199 Average Linkage Rank –​31 Average Linkage Score –​62

47 15 10 6 6 3 2 2 2 2

the water transportation sector in context, the maritime transportation sector is a service sector and is strongly forwarded linked because it provides a service to other sectors in the economy. Furthermore, as outlined above, Ireland is a small, open economy and its island status means that sectors in the wider economy rely heavily on water transportation as a means of importing and exporting goods. Thus, given the structure and geo-​economic status of the country, it is unsurprising that maritime water transportation is an important intermediate service in the production process of Irish industrial and manufacturing sectors. Case Study: Discussion The main objective for developing cluster policies is to improve the business environment, capabilities, and performance of local firms in targeted industries (Doloreux & Shearmur, 2009). The objective of this Case Study was to formally examine the direct and indirect impact of the maritime transportation sector and its potential for cluster formation using tools available to regional scientists. Firstly, using LQs and descriptive statistics on regional marine activity, the analysis found that there is a spatial concentration of the maritime transportation activities in the South East region, particularly in Dublin. Secondly, IO analysis found that the maritime transportation sector had the third highest backward linkage and highest forward linkage in the Irish economy in 2007. In terms of backward linkages the analysis found that in 2007 the sector had high backward linkages with a number of professional and technology-​based services, such as the computer, insurance and banking sectors. Symmetrically, with regard to forward linkages the analysis

 

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showed that the maritime transportation sector was an important input into three of the most economically valuable sectors in the Irish economy. Given the strong linkages to a number of key service sectors already in place, and the large backward linkages to a number of key economic sectors, the future development of a maritime transportation cluster could potentially have large effects on the rest of the economy. Recent research has questioned the current emphasis on cluster formation given that it is rarely supported by empirical economic evidence indicating that the cluster formation would be of benefit to both the sector in question and the wider economy (Midmore et al., 2006; Doloreux & Shearmur, 2009). The objective of this paper was to therefore formally examine the direct and indirect impact of the maritime transportation sector and its potential for cluster formation. With regard to public policy, the results of this Case Study indicate that (a) the maritime sector has strong buyer and supplier linkages and (b) a spatial concentration of maritime transport related firms exist in Ireland. Thus, this analysis indicates that there is a potential for a maritime cluster situated around Dublin Port. This cluster would include shipping operations at its core and with potential linkages to high value added, technology-​based professional services in areas such as banking, law, maritime commerce, ship finance and insurance. A key to understanding the motivation behind industrial cluster policy is the idea that geographic proximity creates the type of collaborations, knowledge spillovers, and positive externalities that firm can use and exploit (Doloreux & Shearmur, 2009). Whilst cooperation between linked companies may occur spontaneously, policies that aim to develop an industry should also focus on support for collaboration between relevant companies (Cooke & Morgan, 1998). Traditionally, policy provision and top down support was seen as a governmental role for cluster development. However, rather than a top down governmental approach, other types of institutional interventions, such as universities, research centers, local authorities and private businesses, may facilitate cooperation (Cooke & Morgan, 1998). The role of central actors within a cluster is to provide a hub where companies, government agencies and researchers can collaborate and stimulate internal R&D. 6.4  DISCUSSION Within Porters model, demand conditions are seen to originate solely within the ‘home base’ of the cluster. Much emphasis is placed on geographical proximity and the national or regional environment as the source of competitive advantage. Cooperation between linked companies may occur spontaneously. However, policies to foster the development of groupings of companies

 



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or industries should also focus on support for cooperation and alliances between relevant companies. However, rather than a top down governmental approach, other types of institutional interventions, public or private, have functioned to facilitate cooperative structures and the development of infrastructure for regulating cooperation (Cooke & Morgan, 1998; Staber, 1996). Steps to support cooperation and alliances can be taken by regional and local authorities as well as national development agencies and networks initiated by both public and private organizations have proven important in the development of the Japanese maritime cluster (Shinohara, 2010) Finally, while there are numerous theoretical and empirical studies on clusters, many of these studies make the implicit assumption that clusters operate and develop along similar lines whatever the regional or industrial context. However, in practice, there is often a significant difference in the way clusters develop and evolve across sectors (Doloreux & Shearmur, 2009). Research on cluster development has revealed singularities in the form, path development, and growth patterns of different clusters based on their sector and region (Isaksen, 2001). As such research indicates that these singularities make it impossible to develop a policy framework or a model of “best-​practice” suited to and effective for every sectoral and regional context (Nauwelaers & Wintjes, 2002).

BIBLIOGRAPHY Alderton, T., & Winchester, N. (2002). Globalisation and de-regulation in the maritime industry. Marine Policy 26: 35–43. Bathelt, H. (2005b) Cluster relations in the media industry: Exploring the ‘distanced neighbour’ paradox in Leipzig, Regional Studies, 39(1): 105–​127. Benito, G., Berger, E., De La Forest, M., Shum, J. (2003). A cluster analysis of the maritime sector in Norway. International Journal of Transport Management 1:  205–​206. Brett, V., Roe, M. (2010). The potential for the clustering of the maritime transport sector in the Greater Dublin Region. Maritime Policy & Management 37(1): 1–​16. Chang, Y. (2011). Maritime clusters: What can be learnt from the South West of England. Ocean & Coastal Management 54: 488–​494. Central Statistics Office (2012). Goods Exports and Imports, Central Statistics Office, Dublin, Ireland. Colgan C. (2013). The ocean economy of the United States: Measurement, distribution and trends. Ocean and Coastal Management, 71: 334–​343. Cooke, P., Morgan, K. (1998). The Associational Economy: Firms, Regions and Innovation. Oxford University Press, Oxford. De Langen, P. (2002). Clustering and performance: the case of maritime clustering in the Netherlands. Maritime Policy & Management 29(3): 209–​221.

 

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De Langen, P. (2004). The Performance of Seaport Clusters; a framework to analyze cluster performance and an application to the seaport clusters. In Durban, Rotterdam and the lower Mississippi, Erasmus Research Institute of Management, Rotterdam School of Management /​Rotterdam School of Economics, Erasmus University Rotterdam. Doloreux, D., Shearmur, R. (2009). Maritime clusters in diverse regional contexts: the case of Canada. Marine Policy 33: 520–​527. Duranthon, G. (2011). California dreamin’: The feeble case for cluster policies. Review of Economic Analysis 3(1): 3–​45. Driffield, N., Munday, M., Roberts, A. (2002). Foreign direct investment, transactions linkages, and the performance of the domestic sector. International Journal of the Economics of Business 9: 335–​351. Eriksson, R. H. (2011). Localized spillovers and knowledge flows. How does proximity influence the performance of plants? Economic Geography, 87: 127–​152. Fingleton B., Igliori D., & Moore, B. (2005). Cluster Dynamics: New Evidence and Projections for Computing Services in Great Britain, Journal of Regional Science, 45:  283–​311. Gruber, S., & Soci, A. (2010). ‘Agglomeration, Agriculture, and the Perspective of the Periphery’, Spatial Economic Analysis, 5(1): 43–​72. Hsieh, P., Li, Y. (2009). A cluster perspective of the development of the deep ocean water industry. Ocean and Coastal Management 52: 287–​293. Hervas-​Oliver, J. L., Gonzalez, G., Caja, P., & Sempere-​Ripoll, F. (2015). Clusters and industrial districts: Where is the literature going? Identifying emerging sub-​ fields of research. European, Planning Studies, 23(9): 1827–​1872. Isaksen A. (2001), Building regional innovation systems: is endogenous industrial development possible in the global economy? Canadian Journal of Regional Science 24: 101–​120. Jacobs, D., De Man, A. P. (1996). Clusters, industrial policy and firm strategy: a menu approach. Technology Analysis and Strategic Management 8(4): 425–​437. Ketels, C. (2003). The Development of the cluster concept–​present experiences and further developments. In NRW conference on clusters, Duisburg, Germany (Vol. 5). Kildow, J. T., McIlgorm, A. (2010). The importance of estimating the contribution of the oceans to national economies. Marine Policy 34, 367–​374. Knarvik, K. H. M., Steen, F. (1999). Self-​reinforcing agglomerations? An empirical industry study. The Scandinavian Journal of Economics 101(4): 515–​532. Kwak, S. J., Yoo, S. H., Chang, J. I. (2005). The role of the maritime industry in the Korean national economy: an input–​ output analysis. Marine Policy 29(3):  371–​383. Lazzeretti L., & Capone F. (2010). Mapping shipbuilding clusters in Tuscany: Main features and policy implications. Maritime Policy & Management 37(1): 37–​52. Martin R., & Sudley, P. (2003). Deconstructing clusters: Chaotic concept or policy panacea. Journal of Economic Geography, 3: 5–​35. Malmberg, A., & Power, D. (2005). (How) do (firms in) clusters create knowledge? Industry and Innovation 12(4): 409–​431.

 



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Midelfart-​Knarvik, K. H., Steen, M. (2002). Delocation and European integration: is structural spending justified? Economic Policy 17: 321–​360. Midmore, P., Munday, M., Roberts, A. (2006). Assessing industry linkages using regional input–​output tables. Regional Studies 40(3): 329–​343. Morrissey, K., & O’Donoghue, C. (2012). The Irish marine economy and regional development. Marine Policy 36, 358–​364. Morrissey, K., O’Donoghue, C. (2013a). The role of the marine sector in the Irish national economy: an input-​output analysis. Marine Policy 37: 230–​238. Morrissey, K., & O’Donoghue, C. (2013b). The potential for an Irish maritime transportation cluster. Ocean and Coastal Management 71: 305–​313. Morrissey K., O’Donoghue C., & Hynes, S. (2011). Quantifying the value of multi-​ sectoral marine commercial activity in Ireland. Marine Policy 35: 721–​727. Murphy, G., & Richard, D. (2008). Strategic Review of Irish Maritime transport sector, Irish Maritime Development Office, Harcourt Street, Dublin. Nauwelaers, C., Wintjes, R. (2002). Innovating SMEs and regions: the need for policy intelligence and interactive policies. Technology Analysis & Strategic Management 14:  201–​215. Othman, M., Bruse, G., Hamid, S. (2011). The strength of Malaysian maritime cluster: The development of maritime policy. Ocean and Coastal Management 54:  557–​568. Porter, M. E. (1990). The Competitive Advantage of Nations. MacMillan, London. Porter, M. E. (1998). On Competition. Harvard Business School, Boston. Porter, M. E. (2000). Location, competition, and economic development: Local clusters in a global economy. Economic Development Quarterly 14(1): 15–​34. Porter, M. E. (2003). The economic performance of regions. Regional Studies 37(6–​7):  549–​578. Poon J., Kedron P., & Bagchi-​Sen, S. (2011). Do foreign subsidiaries innovate and perform better in a cluster? A spatial analysis of Japanese subsidiaries in the US, Applied Geography 44: 33–​42. Salvador R. (2014). Maritime clusters evolution. The (not so) strange case of the Portuguese maritime cluster. Journal of Maritime Research 11: 53–​59. Salvador, R., Simões, A., Soares, C. G. (2016). The economic features, internal structure and strategy of the emerging Portuguese maritime cluster. Ocean & Coastal Management 129: 25–​35. Silos, J. M., Piniella, F., Monedero, J., Walliser J. (2012). Trends in the global market for crews: A case study. Marine Policy 36: 845–​858. Shinohara, M. (2010). Maritime cluster of Japan: Implications for the cluster formation policies. Maritime Policy & Management: The Flagship Journal of International Shipping and Port Research 37: 377–​399. Staber, U. (1996). ‘Accounting for Variations in the Performance of Industrial Districts: The Case of Baden-​Wurttemberg.’ International Journal of Urban and Regional Research 20: 299–​315. Titze, M., Brachert, M., & Kubis, A. (2011). The identification of regional industrial clusters using Qualitative Input–​Output Analysis (QIOA). Regional Studies 45: 89–​102.

 

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Trant, J., & Liddane, M. (2010). Weathering the Economic Crisis, European Commission: Directorate-General for Energy and Transport, Brussels. Wijnolst, N., Jenssen, J.I., Sødal, S. (2003). European Maritime Clusters. Foundation Dutch Maritime Network, Cutch Maritime Network Series, Publication 25. Zhang, W., Lam, J. S. L. (2013). Maritime cluster evolution based on symbiosis theory and Lotka–​Volterra model. Maritime Policy & Management 40(2): 161–​176.

 

Chapter Seven

Marine Clusters Specialization or Diversity?

7.1  INTRODUCTION New global opportunities exist in the development of maritime-​based products and services (Morrissey et al., 2011; Zhao et al., 2014). Europe has historically had an important maritime industry with a strong global position in many maritime sub sectors (Viederyte, 2013), including maritime transportation and ship and boat building. However, as globalization increases competition from lower cost locations like South America and Asia, the question has become how to maintain and strengthen the competitiveness of the European maritime sector. Within this context, many European countries have moved in the direction of having their maritime industries represented by cluster organizations (Viederyte, 2013). As noted in ­chapter  6, the policy focus on cluster development in the maritime sector reflects the insights of research in the early nineties by Porter (1990). Porter (1990, 1998) noted that since many industrial activities are quite “footloose” a high spatial concentration of firms in a particular area indicates that these firms benefit from being part of a spatial concentration or a “spatial clique” (Gruber & Soci, 2010; Poon et al., 2011). Although the importance of colocation is widely acknowledged, there is debate as to whether agglomeration economies arise between firms belonging to either the same or to different industries (van der Panne, 2004; Titze et al., 2011). There is a growing body of literature stating that variety is beneficial for economic growth among collocated industries (Titze et al., 2011; Boschma et al., 2013). The development of clusters with a diverse range of sectors may be more advantageous to clusters than a high degree of sector specialization. Further studies expand on this notion of diversity and suggest that new industries are more successful when they evolve from the knowledge and resource base of the existing industries that are related via 107

 

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similar inputs and/​or outputs of production (Steen & Hansen, 2013; Boschma & Frenken, 2011). However, the extent to which firms are related has an important effect on the actual impact of differing knowledge spillovers and learning within a region. The key argument is that spillovers are more fruitful when they occur between sectors that are neither too cognitive proximate nor too cognitive distant (Nooteboom et al., 2007). 7.2  RELATED VARIETY Marshallian agglomeration theory stresses the positive role of localization on external economies, arguing that the sectoral specialization of a region is a positive factor because firms are expected to learn mainly from other local firms in the same industry (Frenken et al., 2007; Gruber & Soci, 2010). Jacobian externalities theory (Jacobs, 1969) suggests that diverse regional economies encourage more knowledge spillovers because firms receive new and better ideas from firms working in many different industries. Much research has focused on whether spillovers occur primarily when a cluster is specialized in a few sectors (localization economies), or diversified into a large variety of sectors (Jacobs externalities). Recent studies in evolutionary economic geography suggest that new industries are more successful when they evolve from the knowledge and resource base of the existing industries that are related via similar inputs and/​or outputs of production (Steen & Hansen, 2013; Boschma & Frenken, 2011). Thus, the research argues that while Jacobian externalities are important, knowledge spillovers will only be effective among complementary sectors with shared competencies (Frenken et al., 2007). Such complementariness is captured by the notion of related variety (Frenken et al., 2007), where it is argued that industries that are cognitively proximate will gain competitive advantage through shared competencies, innovation and knowledge transfer (Frenken & Boschma, 2007). Related variety is based on the idea that novelty is mostly an outcome of knowledge spillovers between sectors with shared and complementary knowledge bases, rather than a result of specialization or diversification. Research has found that the more variety across related sectors in a region the more learning opportunities there are for local industries. This will result in more intersectoral knowledge spillovers and enhanced regional performance (Boschma et al., 2013). However, the extent to which firms are related has an important effect on the actual impact of differing knowledge spillovers and learning within a region. The key argument is that spillovers are more fruitful when they occur between sectors that are neither too cognitive proximate nor too cognitive distant (Nooteboom et al., 2007). That is, some degree of cognitive

 



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proximity between two sectors ensures effective communication and common understanding, and some degree of cognitive distance is needed to avoid cognitive lock-​in (Nooteboom et al., 2007; Boschma et al., 2013). It is not only the stock of inputs, such as common technologies, skills, knowledge and purchased goods and services in an economy that affects growth, but also the precise composition of these inputs. A region specializing in a particular composition of complementary sectors will experience higher growth rates than a region specializing in sectors that do not complement each other (Boschma et al., 2013). Thus, it is not regional diversity (which involves too large cognitive distance) or regional specialization per se (resulting in too much cognitive proximity) that stimulates real innovations, but regional specialization in related variety that is more likely to induce interactive learning and innovation (Asheim et al., 2011). As such, the traditional dichotomy of localization economies and Jacobs’ externalities is too simple (Porter, 2003; Asheim et al., 2011). In the 2000s, the concept of industry relatedness was combined with the empirical observation that knowledge spillovers were often geographically bounded (Boschma et al., 2013). Porter (2003) was one of the first to recognize the importance of spatial externalities across related industries and noted “clusters are important because of the externalities that connect the constituent industries, such as common technologies, skills, knowledge and purchased inputs” (Porter, 2003, p. 562). Porter (2003) argued that it is reasonable to expect that spillovers occur more frequently between close neighbors. Boschma (2005) further noted that geographic proximity is likely to influence the extent to which firms can absorb and use external knowledge from the industry in which the firm operates. While, Eriksson (2011) notes that geographic proximity reduces the potential communication problems that are associated with being located in a diverse local setting. Section 7.3 introduces IMERC a newly established multisectoral maritime cluster in the South West of Ireland. 7.3  MARITIME CLUSTERS AND THE IRISH MARITIME AND ENERGY RESOURCE CLUSTER (IMERC) From a firm’s perspective, cluster development means a shift in focus from the firm to productive systems and understanding competitiveness as a collective result rather than the outcome of individual processes (Fingleton et al., 2005). Although cooperation between linked companies may occur spontaneously, policy provision and support for clusters is traditionally is important. As noted in ­chapter  6 this support is often believed to be a governmental role. However, research has indicated that other types of institutional interventions,

 

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such as universities, research centers, local authorities and private businesses, may facilitate cooperation among firms (Cooke & Morgan, 1998; Calzonetti et al., 2012). Porter (1990, 1998) emphasizes the importance of university led clusters, as an optimal manner in which R&D and innovation can be rapidly shared among cluster participants. With regard to the maritime sector, research in Canada found that clustered maritime firms tend to be more innovative and more frequently involved in R&D and employee training, make more use of informational input from research institutes and universities, and collaborate more intensively with local partners than maritime firms in general (Deloreux & Melançon, 2008). In Italy, Lazzeretti and Capone (2010) examine the cluster characteristics of the shipbuilding industry in Tuscany and find that the ship-​building industry, in particular, the clusters of pleasure and sporting boat building represents an important “made-​in-​Italy” sector to the Tuscan region. Further research on the Malaysian maritime cluster (Othman et al., 2011) also stresses the key role of universities and research centers in the success of the maritime-​based clusters, particularly in disseminating knowledge. Examining the processes in which clusters are formed within the Norwegian Centre of Expertise, the Møre maritime cluster and the Hordaland subsea cluster, Fløysand et al. (2012) found that marine clusters can be both bottom-​up and top-​down in nature. Fløysand et al. (2012) observe that the Møre maritime cluster is characterized by bottom-​up clustering processes and illustrates how the material practices of firms can trigger clustering processes such as the establishment of a cluster and the identification of a prototype of best cluster practice. The Irish Maritime and Energy Resource Cluster (IMERC) established in 2010, is a multisectoral cluster developed across University College Cork (UCC), Cork Institute of Technology (CIT), and the Irish Naval Service (INS). While Ireland’s share of the global maritime sector is small (Morrissey et al., 2011), IMERC was developed to address the growing global opportunities in four maritime subsectors; marine energy; shipping, logistics and transport; maritime safety and security; and yachting goods and services. IMERC encompasses a mix of established sectors such as the maritime transport and emerging sectors such the marine renewable energy sector. Globally the oil and gas exploration sector is a mature sector, marine renewable energy is an embryonic sector with Ireland seen as having the best wave resources. Regarding the shipping, logistics and transport pillar, maritime transportation is a mature industry both in Ireland and internationally. However, large growth potential has been identified around high tech services to shipping companies, particularly in the development of high tech logistics equipment (EU, 2012). The EU’s Blue Growth Study, Blue Growth—​Scenarios and drivers for Sustainable Growth from the Oceans, Seas and Coasts (2012) identifies the maritime safety and security as a key new sector within the

 



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marine economy. In terms of the yachting products and services, the design and building of luxury yachts is a burgeoning sector internationally and one can classify this sector as an emerging sector in Ireland, with high international growth potential. The role of IMERC from an institutional perspective is therefore one that explores and exploits the complementariness of the four pillars in terms of knowledge, labor skills and related demand and supplier costs. This requires stimulating the development of networks, knowledge flows and social capital to link upstream and downstream firms, as well as other organizations, to form a productive and innovative system generating vertical and horizontal spillovers (Viederyte, 2013). In assessing the future impact of the cluster, it is important that IMERC partners have access to appropriate methods to evaluate the relative strengths of the cluster from its onset. As outline above, studies suggest that clusters are more successful when they are related via similar inputs and/​or outputs of production (Steen & Hansen, 2013; Boschma & Frenken, 2011) rather than multidirectional intracluster linkages. Given the multisectoral profile of IMERC, it is important that a method for analyzing the interlinkages or the “relatedness” between each of the four pillars; Marine Energy, Shipping, Logistics and Transport, Maritime Safety and Security, and Yachting Products and Services is available. The next section introduces IO-​based analysis as a method to examine the level of relatedness among the IMERC sub-​sectors. 7.4  METHODS A cluster is composed of several inter and intra-​linked elements: the firms, their collective infrastructure, their respective suppliers and buyers and their research and development (Learmonth et al., 2003). As with individual firms, clusters rely on firms both within and outside their cluster to purchase inputs for their production process, while simultaneously selling output to sectors (sales) to generate profit. A sector’s linkage through its direct and indirect purchases is called its backward linkage. As opposed to backward linkage, a sector is forward linked to other sectors through its direct and indirect sales to them. As noted in section 7.2, research in evolutionary economics has found that an important dimension of cluster interconnectedness is the nature of buyer–​supplier links. Purchasing and sales linkages provide one means for transmitting both codified and tacit knowledge between firms, in terms of technology, skills, products or new management ideas (Midmore et al., 2006). Thus, tighter buyer-​supplier linkages may provide agglomeration externalities (Steen & Hansen, 2013; Boschma & Frenken, 2011) within clusters. Considering the complexity of these interlinked elements, it would

 

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be an enormous task to trace and measure the backward and forward links between each of the firms, as well as their external connections within a cluster (Cai & Lueng, 2002). Developed by the economist Wassily Leontief, IO tables detail the sales and purchases for each sector within an economy for a given year (Learmonth et al., 2003). IO tables can therefore be used to quantify the size, importance and character of a cluster by identifying the scales of input and outputs to other cluster sectors and external cluster sectors (Learmonth et al., 2003; Titze et al., 2011). However, within the IO framework there are a number of approaches that may be used to measure relatedness (Titze et al., 2011). These include a qualitative input-​output analysis (Titze et al., 2011), an eigenvector method (Midmore et al., 2006) and a principal components factor analysis. However, as with most quantitative analysis the method one can use is often based on the data one has to hand. To examine the intersectoral linkages between the IMERC pillars this chapter will once again use the marine disaggregated IO table outlined in ­chapter  2. Using this augmented IO table, the analysis presented here is similar to the direct flow analysis proposed by Bijnen (1973) and focuses on quantifying the strength of both backward and forward linkages within and across IMERC. However, it is important to note the limitations of both the data this paper proposes to use and the IO framework more widely with regard to examining relatedness among sectors. First, the augmented marine IO table uses data from 2010 and is therefore somewhat out of date. However, it is important to note that IMERC was established in 2010. Thus, the use of 2010 date to provide an ex-​ante evaluation of the relatedness between the four pillars is still relevant. Second, although the augmented IO table developed by Morrissey and O’Donoghue (2013b) includes ten additional marine subsectors, an immediate problem encountered is that one cannot map the cluster as defined for policy purposes to the augmented IO table. Table 7.1 presents the mapping of each of the four IMERC pillars onto its respective marine augmented IO sector. This difficulty occurs because there is a lack of congruence between the cluster designation and sectors as defined under the NACE system of classification. For example, marine energy, which refers to both hydrocarbon and renewable marine energy, is represented by oil and gas extraction. A renewable energy sector does not exist within the augmented marine IO table. In contrast, the shipping, logistics and transport may be divided into water transport services (shipping activities) and auxiliary water transport services, which include port activities, stevedoring, and so on. Maritime safety and security is linked to public administration and defence as a means of representing the naval service activities within the cluster, whilst yacht products and services are represented by the boat building

 



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Table 7.1  Map of Each of the Four Imerc Pillars onto its Respective Marine Augmented Io Sector IMERC pillar

NACE name

Marine Energy Shipping, Logistics and Transport

Oil & Gas Extraction Water Transport Auxiliary Services to Water Transport Public Administration & Defence Boat Building

Maritime Safety and Security Yachting Products and Services

sector. Third, only monetary based linkages can be measured with the IO table (Learmonth et al., 2003). This means that many important features of the cluster particularly knowledge spillovers are ignored. Knowledge spillovers are crucial to innovation, but it is unclear on how knowledge spillovers are made and happen (Steen & Hansen, 2013; Boschma & Frenken, 2011). Examining a number of Scottish clusters, Learmonth et al. (2005) state that if the flows of education, innovation and labor knowledge is linked to the flows of goods and services between sectors, the IO table is not as restricted in terms of nonmonetary flows as originally assumed. Further, research by evolutionary economic geographers (Boschma & Frenken, 2006, 2011; Neffke et al., 2011) has found that knowledge tends to flow between sectors that are related via similar inputs and/​or outputs of production. Thus, while this paper focuses on quantifying the tangible linkages between the IMERC pillars, one can argue that the flow of inputs and outputs within a cluster may also be seen as a rudimentary proxy for knowledge flows. Finally, research by Neffke et al. (2011) indicates that industries have different agglomeration needs over different stages of their life cycles. This is because their mode of competition, innovation intensity, and learning opportunities change over time. Classifying industries as young, intermediate, or mature, Neffke et al. (2011) found that Marshallian externalities steadily increase with the maturity of industries. However in contrast the effects of local diversity (Jacobs’ externalities) are positive for young industries, but decline and even become negative for more mature industries. Given the cross sectional nature of the data used, this paper is unable to produce a dynamic perspective regarding IMERCs life-​course. While the IO methodology has clear limitations, the simplicity of using an IO table for linkage analysis and the availability of a marine augmented IO table for Ireland means that the method is an interesting starting point for measuring relatedness within IMERC. Using the augmented IO table (Morrissey & O’Donoghue, 2013a, b) the next section quantifies both inter-​and intralinkages across IMERC.

 

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7.5  RESULTS The Four IMERC Pillars and the Wider Economy This section demonstrates how the augmented IO table can be used to (a) quantify the linkages between the cluster and the wider economy and (b) intracluster linkages in terms of shared inputs and outputs among the four IMERC pillars. To quantify the linkages between IMERC and the wider economy, table 7.3 presents the overall backward linkages for each of the IMERC pillars. Of the IMERC pillars, maritime transportation has the highest (109) and boat building the second highest backward linkage (73). Using the marine augmented IO table ­chapter 6 found that the maritime transportation has the third highest backward linkage (1.09) within the wider Irish economy. This implies that for every €1 produced within the water transportation sector, €0.09 is backward linked to its direct and indirect upstream suppliers. A backward linkage of greater than one implies that the sector is an important input supplier to other downstream sectors. Table 7.2 indicates that of the IMERC pillars, maritime transportation (120) and auxiliary maritime transport services (95) have the highest forward linkage and second highest linkages respectively. Chapter 6 also found that the maritime transportation sector had the seventh highest forward linkage within the Irish economy with a value of 1.20 in 2007. This implies that every €1 produced by the maritime transportation sector is forward linked to €0.20 to the production of the sectors direct and indirect downstream demanders. The high forward linkage of the maritime transportation sector reflects the fact that the service provided by the sector is primarily consumed as part of the intermediate production process by consuming industries. It is important to note that the small Table 7.2  Overall Backward and Forward Linkages for Each of the Imerc Pillars IMERC pillar

IO sector

Multiplier

Backward Linkages for each of the IMERC Pillars Marine Energy Oil & Gas Extraction Shipping Water transport services Shipping Logistics & Transport Auxiliary Maritime Transport Services Maritime Safety and Security Public Administration & Defence Yachting Products & Services Boat Building

44 109 44 52 73

Forward Linkages for each of the IMERC Pillars Marine Energy Oil & Gas Extraction Shipping Water transport services Shipping Logistics and Transport Auxiliary Maritime Transport Services Maritime Safety and Security Public Administration & Defence Yachting Products and Services Boat Building

62 120 95 11 1

 



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forward linkages for the boat building sector and public administration and defence reflect the fact that the goods produced for this sector are sold for final consumption. Relatedness Among the IMERC Pillars More central to this paper and demonstrating the effectiveness of IO tables as a cluster analysis tool, table 7.4 presents the top five sectors in terms of purchases (backward linkages) for each of the IMERC pillars. For the marine energy sector (NACE, oil and gas sector), one can see that 25 percent of its backward linkages (sectors that they buy their goods and services from) are associated with financial intermediation services, 11 percent with electricity and gas, 9 percent with both other business services and land transport services respectively and 7 percent with the computer and related services industry. With regard to the yachting products and services sector (NACE: boat building), 19 percent of its backward linkages are associated with the insurance sector, 11 percent with the financial intermediation sector, 8 percent with the construction sector, 7 percent with hotels and accommodation and 7 percent with medical, precision and optical instruments. Examining the shipping, logistics and transport sector (NACE: water transportation sector), linkages are highest with the water transportation sector itself 43 percent, auxiliary transport services (17 percent), computer and related services, 7 percent, financial intermediation services and auxiliary marine transportation services 4 percent, respectively. In terms of auxiliary maritime transportation services (NACE: auxiliary marine transportation services), table 7.3 indicates that this sector has the strongest backward linkages with other business services, computer and related services and auxiliary transport services, 13 percent; water transport, 11 percent and the financial intermediation sector 7 percent. Finally, for the maritime safety and security (NACE: public administration and defence), the sector has the highest backward linkages with the construction (17 percent), other business services (13 percent), real estate (12 percent), computer and related services and post and telecommunications sector (7 percent). From table 7.3, it can be seen that the four pillars within IMERC (represented as five sectors for the purpose of this research) have relatively weak intracluster links, with the closest links between the maritime transportation sector and auxiliary maritime transportation service sector. Table 7.4 indicates that a number of the IMERC pillars share the same inputs to production. These include financial Intermediation services (linked to four of the five IMERC pillars), computer and related services (linked to four of the five IMERC pillars) and other business services (linked to three of the five IMERC pillars) sectors. The shipping, logistic and transport sector,

 

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Table 7.3  Top Five Backward Linkages of Each of the Imerc Pillars Disaggregated by Nace Code Oil & Gas Extraction Computer and related services Land transport services Other business services Electricity and gas Financial intermediation services

7% 9% 9% 11% 25%

Boat Building Medical, Precision and Optical Instruments Hotel and restaurant services Construction work Financial intermediation services Insurance and pension services

7% 7% 8% 11% 19%

Maritime Transportation Auxiliary Maritime Transport Services Financial intermediation services Computer and related services Auxiliary transport services & travel agencies Maritime Transportation

3.7% 3.7% 7.3% 16% 43%

Auxiliary Maritime Transport Services Financial intermediation services Maritime Transportation Auxiliary transport services & travel agencies Computer and related services Other business services

7% 11% 13% 14% 14%

Public Administration & Defence Post and Telecommunications Computer and related services Real estate services Other business services Construction work

9.6% 10% 12% 13% 17%

broken into shipping operations and auxiliary maritime transport services for the purpose of this research, as noted above, are closely backward linked and also share similar input suppliers across sectors. As a whole, one notes that the four pillars of IMERC have the strongest backward links with service-​ based sectors, particularly high value added sectors such as the financial and computer sectors. While there is no indication in the literature on what kind of numbers indicates a high level of relatedness within a cluster, these results are important qualitatively. As noted by Frencken et al. (2007) the underlying qualitative nature of economic development, for example, in terms of the variety of

 



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Table 7.4  Top Five Forward Linkages of Each of the Imerc Pillars Disaggregated by Nace Code Oil & Gas Extraction Wholesale trade Petroleum & Other manufacturing products Electricity and gas Basic metals Construction work Boat Building Water Based Activities Recreation Air transport services Boat Building Other transport equipment

3% 8% 13% 23% 23% Less 1% 1% 5% 10% 39%

Water transport services Construction work Auxiliary transport services & travel agencies Post and telecommunication services Wholesale trade Auxiliary Maritime Transport Services

5% 5% 8% 13% 39%

Auxiliary Maritime Transport Services Post and telecommunication services Construction work Auxiliary transport services & travel agencies Water transport services Wholesale trade

4% 5% 9% 17% 25%

Public Administration & Defence Land transport services Real estate services Other business services Health and social work services Construction work

9% 9% 9% 9% 27%

sectors in a region or a cluster, have been rarely addressed. This means that not only the stock of inputs affects growth, but also the precise composition in a qualitative sense (Frenken et al., 2007). Thus, the aim of this chapter is to examine the buyer–​seller linkages within IMERC from a compositional perspective. Furthermore, while the marine sector is often seen as a “traditional” primary production oriented sector, these results indicate that the marine sectors within IMERC have the strongest linkages with service-​ based sectors. In terms of knowledge spillovers based on shared inputs to the process of production, the analysis demonstrates the strong input links between each of the IMERC pillars that may be exploited from increased cluster-​based cooperation.

 

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Continuing the analysis, table 7.4 provides the five sectors with the highest sales (forward linkages) for each of IMERC’s pillars. For the marine energy sector, one can see that 23 percent of its forward linkages (sectors that they sell their goods and services to) are associated with construction, 8 percent, basic metals (23 percent), electricity and gas (13 percent), petroleum and other manufacturing products (8 percent) and wholesale trade (3 percent). With regard to the yachting products and services sector, forward linkages are highest with other transport equipment sector (39 percent), boat building itself (10 percent), air transport (5 percent), Recreation (1 percent) and Water-​Based Activities (less than 1 percent). Examining the shipping, logistics and transport sector; its forward linkages are highest with the auxiliary maritime transport sector (39 percent), wholesale trade (13 percent), post and telecommunications (8 percent), auxiliary transport services (5 percent) and water transport itself (5 percent). Table 7.4 indicates that the auxiliary maritime transportation services sector has the strongest forward linkages with wholesale trade (25 percent), water transport (17 percent), auxiliary transport services (9 percent), construction (5 percent), and post and telecommunications (4 percent). Finally, the maritime safety and security sector has the highest forward linkages with the construction sector (27 percent), Housing and Social Welfare (9 percent), Other Business Services (9 percent), Real Estate (9 percent) and Land transport (9 percent). With regard to relatedness and outputs, table 7.4 indicates that a number of IMERC sectors deliver outputs to the same sectors. These include wholesale trade (linked to three of the five IMERC pillars), construction (linked to three of the five IMERC pillars) and post and communications (linked to two of the five IMERC pillars) sectors. While, it is important to note once again that the small input linkages for the boatbuilding and public administration and defence reflect the fact that the goods produced in this sector are sold for final consumption. 7.6  DISCUSSION Agglomeration and clustering are seen as fundamental causes of an enhanced level of local economic development, creating externalities that cause firms to grow faster and larger than they otherwise would (Fingleton et al., 2005). This is evident from the remarkable ability of some regions to sustain above-​average economic performance. However, the processes underlying the success of clusters including Silicon Valley or the Biomedical cluster in Galway Ireland, are still largely unknown and subject to debate (van der Panne, 2005). Competitive advantage within clusters arises as a result of the Marshallian idea that geographic proximity creates the type of collaborations, knowledge spillovers, and positive externalities that companies can use and

 



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exploit (Gruber & Soci, 2010; Eriksson, 2011). However, an important debate remains as to whether agglomeration economies arise between firms belonging to either the same or to different industries (van der Panne, 2004; Titze et al., 2011). Previous research has found that regional policies based on supporting related variety reduce the risk of selecting wrong activities because one takes existing regional competences as building blocks to broaden the economic base of the region. Co-​located in the Lower Cork Harbor, IMERC is a mix of established marine sectors such as the offshore exploitation and production of oil and gas and the maritime transportation sector and emerging sectors such as marine renewable energy and sensing technology. These sectors bring together a highly heterogeneous set of actors with skills and capabilities ranging from engineering and construction to advanced computer modeling and technical design to navigating vessels at sea. The conventional representation of a cluster is a self-​supporting group of firms with a nest of multidirectional intracluster linkages (Learmonth et al., 2003). The IO analysis presented in this paper found that the four pillars within IMERC (represented as five sectors for the purpose of this research) have relatively weak intracluster links, with the closest links between the maritime transportation sector and auxiliary maritime transportation service sector. However, work on clusters note that weak intermediate linkages in inputs and outputs within clusters do not necessarily imply a weak cluster (Learmonth et al., 2003). Recent research by evolutionary economic geographers (Boschma & Frenken, 2006, 2011; Neffke et al., 2011) has found that knowledge tends to flow between sectors that are related via similar inputs and/​or outputs. New industries are more successful when they evolve from the knowledge and resource base of the existing industries that are related via similar inputs and/​or outputs of production (Neffke et al., 2011; Boschma & Frenken, 2011; Steen & Hansen, 2013). The IO analysis found that a number of the IMERC pillars share the same buyer–​seller links. With regard to input/​backward linkages these include financial Intermediation services (linked to four of the five IMERC pillars), computer and related services (linked to four of the five IMERC pillars) and other business services (linked to three of the five IMERC pillars) sectors. The IO analysis also found that a number of IMERC sectors deliver outputs to the same sectors. These include wholesale trade (linked to three of the five IMERC pillars), construction (linked to three of the five IMERC pillars) and post and communications (linked to two of the five IMERC pillars) sectors. With regard to how these findings relate to the future of IMERC and Irish regional development, previous research in Spain on the impact of related variety on regional development found that related variety enhances employment growth (Boschma et al., 2013). Relatedness was found to be high between the IMERC pillars, particularly among high knowledge, high

 

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added value sectors such as financial intermediation and computer and related services. This result indicates that the heterogeneous mix of sectors within IMERC should aid Morrissey (2014) employment forecasts for the cluster to 2025. Research by Neffke et al. (2011) indicates that industries have different agglomeration needs in different stages of their life cycles because their mode of competition, innovation intensity, and learning opportunities change over time. Placing these findings in an industrial lifecycle classification, this paper found that the four IMERC pillars are predominately young industries, anchored by one mature sector, the shipping sector. Neffke et al. (2011) found that Marshallian externalities steadily increase with the maturity of industries. However, in contrast the effects of local diversity (Jacobs’ externalities) are positive for young industries. Thus, given the predominately young nature of each of IMERCs pillars a heterogeneous rather than specialized cluster should have a positive impact on the economic development of the cluster. From a methodological perspective, no single analytical method has been deemed appropriate to analysis the level of relatedness within a cluster. The analysis found that linkage analysis based on the IO framework is a relatively simple and useful tool that can be used to quantify the linkages between sectors in a cluster. With regard to the usefulness of IO tables in capturing non-​monetary flows in a cluster, particularly information, this paper argues that similar to Learmonth et al. (2003) that if the flows of nonmarket linkages within a cluster are linked to the flows of goods and services between sectors, the IO table is not as restricted in terms of nonmonetary flows as originally assumed. The results of this paper also provide an initial map of knowledge linkages based on shared inputs and outputs within IMERC that may be integrated within further research on knowledge flows within clusters. The outcomes presented in this paper require further research, both empirically and methodologically. Similar to Boschma et al. (2013) this paper argues that there is a need to further explore the source of agglomeration externalities. There is some evidence that labor flows between related industries are of particular importance (Boschma et al., 2009), but more systematic evidence is needed. This paper argues that a more qualitative research methodology such as the qualitative IO approach used by Titze et al. (2011), combined with employment data may be a suitable starting point. Finally, it is also important to link these findings within the context of ­chapter  6. Along with research by Brett and Roe (2010), ­chapter  6 found that there is potential for a maritime cluster within the Dublin region. However, these findings do not conflict with the research found here. The cluster proposed in the Dublin area focuses solely on the sectors directly related to the maritime transportation sector and exploiting the natural comparative advantage of the Dublin port infrastructure. However, more recent studies

 



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on agglomeration and clusters have increasing noted that different industries agglomerate for different reasons (Ellison et al., 2010; Faggio et al., 2014). For instance, in some industries knowledge spillovers are important. Other industries are driven by labor market pooling or input sharing. Within this context, IMERC is situating the maritime transportation sector within the wider marine economy in the South West region. Thus, while a maritime cluster in the Dublin region would seem to prosper by focusing on maritime transport activities alone, similar to Marshall’s specialization theory of cluster organization, IMERC proposes to create comparative advantage based on the notion of related variety within clusters (Frenken et al., 2007). BIBLIOGRAPHY Asheim, B., Boschma, R., Cooke, P. (2011). Constructing regional advantage: Platform policies based on related variety and differentiated knowledge bases. Regional Studies 45(7): 893–​905. Benito, G., Berger, E., De La Forest, M., Shum, J. (2003). A cluster analysis of the maritime sector in Norway. International Journal of Transport Management 1:  205–​206. Bijnen, E. J. (1973). Cluster Analysis, Survey and Evaluation of Techniques. Tilburg University Press, Tilburg. Boschma, R. A. (2005). Proximity and innovation: A critical assessment. Regional Studies 39: 61–​74. Boschma, R., Frenken, K. (2011). Technological relatedness and regional branching. In: Bathelt, H., Feldman, M. P., Kogler, D. F. (eds) Dynamic Geographies of Knowledge Creation and innovation. Routledge, Taylor and Francis. Boschma, R., & Frenken, K. (2006). Applications of evolutionary economic geography, DRUID Working Paper Number  06–​26. Boschma, R., Eriksson, R., & Lindgren, U. (2009). How does labour mobility affect the performance of plants? The importance of relatedness and geographical proximity. Journal of Economic Geography 9: 169–​190. Boschma, R., Minondo, A. & Navarro, M. (2013). The Emergence of New Industries at the Regional Level in S pain: A Proximity Approach Based on Product Relatedness. Economic Geography, 89(1): 29–​51. Brett, V., Roe, M. (2010). The potential for the clustering of the maritime transport sector in the Greater Dublin Region. Maritime Policy & Management 37(1): 1–​16. Cai, J., Leung, P. (2002). The linkages of agriculture to Hawaii’s economy. Economic Issues EI-​4,  1–​8. Calzonetti, F., Miller, D., Reid, N. (2012). Building both technology-​intensive and technology-​limited clusters by emerging research universities: the Toledo example, Applied Geography 34(1): 265–​273. Chang, Y. (2011). Maritime clusters: What can be learnt from the South West of England, Ocean & Coastal Management 54: 488–​494.

 

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Colgan, C. (2013) The ocean economy of the United States: Measurement, distribution and trends. Ocean and Coastal Management 71: 334–​343. Cooke, P., Morgan, K. (1998). The Associational Economy: Firms, Regions and Innovation. Oxford University Press, Oxford. De Langen, P. (2002). Clustering and performance: The case of maritime clustering in The Netherlands. Maritime Policy & Management 29(3): 209–​221. Doloreux, D., Melançon, Y. (2008). On the dynamics of innovation in Quebec’s coastal maritime industry. Technovation 28, 231–​ 243. DOI: 10.1016/​ j.technovation.2007.10.006 Doloreux, D., Shearmur, R. (2009). Maritime clusters in diverse regional contexts: the case of Canada. Marine Policy 33: 520–​527. Duffy, D., Timoney, K. (2013). Quarterly Economic Commentary, Spring 2013. ESRI Forecasting Series, ESRI, Dublin. Ellison, G., Glaeser, E., Kerr, W. (2010). What causes industry agglomeration? Evidence from coagglomeration patterns. American Economic Review 100: 1195–​1213. Eriksson, R. H. (2011). Localized spillovers and knowledge flows. How does proximity influence the performance of plants? Economic Geography 87: 127–​152. European Commission (2012) Blue Growth—​Scenarios and drivers for Sustainable Growth from the Oceans, Seas and Coasts, European Commission, Brussels. Faggio, G., Olmo, S., Strange, W. (2014). Heterogeneous Agglomeration. SERC Discussion Paper 152, London School of Economics. Fingleton, B., Igliori, D., Moore, B. (2005). Cluster dynamics: New evidence and projections for computing services in Great Britain. Journal of Regional Science 45: 283–​311. Fløysand, A., Jakobsen, S. E. & Bjarnar, O. (2012). The dynamism of clustering: Interweaving material and discursive processes. Geoforum 43(5): 948–​958. Frenken, K., Boschma, R. (2007). A theoretical framework for evolutionary economicgeography: Industrial dynamics and urban growth as a branching process. Journal of Economic Geography 7: 635–​649. DOI: 10.1093/​jeg/​lbm018 Frenken, K., van Oort, F. G., Verburg, T. (2007). Related variety, unrelated variety and regional economic growth. Regional Studies 41: 685–​697. Gruber, S., Soci, A. (2010). Agglomeration, agriculture, and the perspective of the peri­ phery. Spatial Economic Analysis 5(1): 43–​72. DOI: 10.1080/​17421770903511353 Jacobs, J. (1969). The Economy of Cities. Random House, New York. Kalaydjian, R. (2011). French Marine-​ related Economic data, 2009. Marine Economics Department, IFREMER, Brest, France. Kildow, J. T., McIlgorm, A. (2010). The importance of estimating the contribution of the oceans to national economies. Marine Policy 34: 367–​374. Kwak, S. J., Yoo, S. H., Chang, J. I. (2005). The role of the maritime industry in the Korean national economy: an input–​output analysis. Marine Policy 29(3): 371–​383. Lazzeretti L., & Capone F. (2010). Mapping shipbuilding clusters in Tuscany: Main features and policy implications. Maritime Policy & Management 37(1): 37–​52. Learmonth, D., Munro, A., Swales, K. (2003). Multi-​sectoral cluster modelling: The evaluation of Scottish enterprise cluster policy. European Planning Studies 11(5):  567–​584.

 



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Marine Institute (2012). Our Ocean Wealth, Towards an Integrated Marine Plan for Ireland, Part II Sectoral Briefs, Marine Institute. Midmore, P., Munday, M., & Roberts, A. (2006). Assessing industry linkages using regional input–​output tables. Regional Studies 40(3): 329–​343. Morrissey, K., O’Donoghue, C., Hynes, S. (2011). Quantifying the value of multi-​sectoral marine commercial activity in Ireland. Marine Policy 35: 721–​7. DOI:  10.1016/​j.marpol.2011.02.013 Morrissey, K., O’Donoghue, C. (2013a). The potential for an Irish maritime transportation cluster: an input–​output analysis. Ocean and Coastal Management 71: 305–​ 313. DOI: 10.1016/​j.ocecoaman.2012.11.001 Morrissey, K., O’Donoghue, C. (2013b). The role of the marine sector in the Irish national economy: An input-​output analysis. Marine Policy 37: 230–​238. DOI:  10.1016/​j.marpol.2012.05.004 Morrissey, K. (2014). Economic Study of the Job Creation Potential arising from Ireland’s Maritime and Energy Cluster, IMERC. University of Liverpool, Liverpool. Morrissey, K., O’Donoghue, C., Farrell, N. (2014). The local impact of the marine sector in Ireland: A spatial microsimulation analysis. Spatial Economic Analysis 9(1):  31–​50. Neffke, F., Henning, M., Boschma, R. (2011). How do regions diversify over time? Industry relatedness and the development of new growth paths in regions. Economic Geography 87(3): 237–​265. DOI: 10.1111/​j.1944-​8287.2011.01121.x Neffke, F., Henning, M., Boschma, R., Lundquist, K.-​J., Olander, L. O. (2011). The dynamics of agglomeration externalities along the life cycle of industries. Regional Studies 45(1): 49–​65. DOI: 10.1080/​00343401003596307 Nooteboom, B., Van Haverbeke, W., Duysters, G., Gilsing, V., van den Oord, A. (2007). Optimal cognitive distance and absorptive capacity. Research Policy 36(7):  1016–​1034. Othman, M., Bruse, G., Hamid, S. (2011). The strength of Malaysian maritime cluster: The development of maritime policy. Ocean and Coastal Management 54:  557–​568. Peng, B., Hong, H., Xue, X., Jin, D. (2006). On the measurement of socioeconomic benefits of integrated coastal management (ICM): Application to Xiamen, China. Ocean & Coastal Management 49: 93–​109. Porter, M. E. (1990). The Competitive Advantage of Nations. MacMillan, London. Porter, M. E. (1998). On Competition. Harvard Business School, Boston. Porter, M. E. (2003). The economic performance of regions. Regional Studies 37(6–​7):  549–​578. Poon, J., Kedron, P., Bagchi-​Sen, S. (2011). Do foreign subsidiaries innovate and perform better in a cluster? A spatial analysis of Japanese subsidiaries in the US. Applied Geography 44: 33–​42. DOI: http://​dx.doi.org/​10.1016/​j.apgeog.2013.07.007 Pugh, D. (2008). Socio-​Economic Indicators of Marine-​Related Activities in the UK Economy. The Crown Estate, London. Shinohara, M. (2010). Maritime cluster of Japan: Implications for the cluster formation policies. Maritime Policy & Management: The Flagship Journal of International Shipping and Port Research 37: 377–​399.

 

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Steen, M., Hansen, G. (2013). Same Sea, Different Ponds: Cross-​ Sectorial Knowledge Spillovers in the North Sea. European Planning Studies. DOI:10.1080/​ 09654313.2013.814622 Titze, M., Brachert, M., Kubis, A. (2011). The identification of regional industrial clusters Using Qualitative Input-​ Output Analysis (QIOA). Regional Studies 45: 89–​102. DOI: 10.1080/​00343400903234688 van der Panne, G. (2014). Agglomeration externalities: Marshall versus Jacobs. Journal of Evolutionary Economics 14:  593–​604. DOI:  10.1007/​s00191-​004-​0232-​x Viederyte, R. (2013). Maritime cluster organizations: enhancing role of maritime industry development. Procedia-​ Social Behaviour Science 81: 624–​ 631. DOI:  10.1016/​j.sbspro.2013.06.487 Zhao, R., Hynes, S., He, G. S. (2014). Defining and quantifying China‫׳‬s ocean economy. Marine Policy 43: 164–​173. DOI:10.1016/​j.marpol.2013.05.008

 

Chapter Eight

From National to Regional to Local A Spatial Microsimulation Model for the Marine

8.1  INTRODUCTION The changing dynamics of regional and local labor markets during the last two decades (Braunerhjelm et al., 2000) has led to increasing levels of employment and income inequalities between and within regions (Ballas & Clarke, 2001; Perugini & Martino, 2008; Rodríguez-​Pose & Tselios, 2009). As outlined in ­chapter  4, the onset of “footloose” capital and labor has created what New Economic Geography has termed a core–​periphery structure (Krugman, 1991). Under the core–​periphery dichotomy some regions are seen as industrial cores and centers of employment and output. In contrast, the remaining regions are deemed peripheral and their role involves supplying natural resource based products to the core and importing industrial goods from the core region (Gruber & Soci, 2010). Within these models, the natural resource sector is taken as immobile, without product differentiation, innovation or knowledge externalities (Gruber & Soci, 2010). However, this book has outlined that the marine sector is increasingly characterized by high-​tech, service-​oriented firms that can supply goods and services to the marine sector irrespective of location (Morrissey et al., 2011; Morrissey & O’Donoghue, 2012). Furthermore, for many of these companies, marine-​based products and services are part of a broad portfolio of activity across various industrial sectors (Morrissey et al., 2011; Kildow & McIlgorm, 2010). These firms are thus more responsive to policy incentives and initiatives than other resource-​ based sectors. Thus, one could argue that policy analysis in the marine sector is better suited to a modeling framework, which emphasizes the heterogeneity of the sector at the microlevel rather than aggregated macrolevel processes. Coupling this firm-​level environment with an increased impetus on marine spatial planning for commercial and environmental sustainability and the 125

 

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development of new ocean-​based sectors (O’Mahony et al., 2009; Rodríguez-​ Rodríguez et al., 2016; Ban et al., 2013) has meant that policymakers and practitioners require methods and techniques capable of estimating the impact of the marine sector at the local level. Chapters 2, 6, and 7 of this book have demonstrated the usefulness of IO analysis to estimate the direct and indirect impact of the marine sector at the national level. Linking IO analysis with regional level data on industrial output and consumption, along with Location Quotients in ­chapter  5 further demonstrated the power of regional level analysis in understanding the marine economy. However, these methodologies only examine the impact of the sector at the national and regional level and are unable to estimate the contribution of the sector to household welfare at the local level. Thus, to further examine the welfare contribution of the sector, data on both the location of the businesses (where the income is generated) and the residential location of workers (where the income is spent) is required. There are a number of datasets that contain one or more of the necessary characteristics to model the contribution of the marine sector to household welfare. However, all the necessary data are not currently contained in one dataset. To address the need for local-​level welfare analysis in the marine sector, this paper proposes the use of spatial microsimulation. Microsimulation models are designed to initially create data at the individual or household scale if such data is missing from available datasets. Once developed, the data from microsimulation models may be used to simulate the distributional impact of differing policies or a change in policy at the micro-​level (Ballas et al., 2006a, b). Microsimulation has a long tradition in economics (Orcutt, 1957). However, national level microsimulation models, like their mesolevel counterparts are unable to examine the distributional impact of policies at the regional or local-​level. Spatial microsimulation models offer a geo-​referenced alternative to aggregate models (Morrissey et al., 2013; Ballas & Clarke, 2001). Spatial microsimulation models can be used to explore spatial relationships and to examine the spatial impact of different policy scenarios at the national, regional and subnational level. As such these models have been used to examine a host of public policy areas to date, including health, (Smith et al., 2011, Edwards & Clarke, 2009, Tomintz et al., 2008), deprivation (Birkin & Clarke, 1989, Gong et al., 2011, Tanton et al., 2009) and resource use (Druckman & Jackson, 2008; Williamson, 2001). In terms of natural resources management, a number of papers have used spatial microsimulation to examine the impact of agricultural policies on public welfare (Ballas et al., 2006a, b; Hynes et al., 2009a) and agri-​environmental policies on land-​based management and conservation (Hynes et al., 2009a, b; Pfeifer et al., 2012). This chapter is based on an article by Morrissey et al. (2014) which used spatial microsimulation to examine the impact of the marine sector at the local level in Ireland. Section 8.2 introduces the spatial microsimulation

 



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methodology and its use in public policy. Section 8.3 outlines the data used to develop SMILE-​marine. Using the Irish marine sector as a Case Study, Section 8.4 outlines the welfare impact of the marine sector at the small area level in Ireland. Section 8.5 offers concluding comments. 8.2  SPATIAL MICROSIMULATION Most government policies have a geographical impact, irrespective of whether they are geographically targeted or not. The redevelopment of international trade theory to encompass increasing returns to scale (Krugman, 1980) and the addition of worker mobility (Krugman, 1991; Krugman & Venables, 1995) to economic geography models has resulted in this “geographical impact” being recognized as a crucial factor in the development (Harris et al., 2011) and welfare of regions. Thus, the complex spatial dynamics, which underlie markets, call for sophisticated tools to help in the formulation and evaluation of appropriate and effective public policies. To formulate such policies, it is necessary not only to understand the nature and the operation of differing sectors at a macro level but also to evaluate the likely impact of these policies on activity at the local level. In particular, there is a need to understand, estimate, or predict which individuals (given their demographic and socio-​ economic characteristics) and localities are most likely to benefit from a change in policy. Policy-​relevant modeling is a challenging research area, which many researchers believe is better suited to a modeling framework which emphasizes individual-​level processes at the local level rather than aggregated process at the macrolevel (see more discussion in Ballas and Clarke, 2001). At the same time, these models need to be underpinned by data that allows the spatial impact of policy decisions to be examined (Ballas et al., 2007). Microsimulation models are designed to initially create data at the individual or household scale if such data is missing from available datasets. For example, while census data includes a variety of socio-​economic variables, such as age, marital status, education, and, importantly, a geographical component. However, variables such as income level, personal pension information, and health status are usually not included due to data confidentiality (although there are exceptions in different countries). As such, using the census of population for explanatory research is often restrictive due to missing data. In contrast, individual/​household survey-​based datasets often contain more income-​related and socio-​economic variables. However, due to the cost and administrative difficulties in collecting survey data, surveys are usually small in scale and can be misrepresentative of the general population. However, national level microsimulation models, like their mesolevel counterparts are

 

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unable to examine the distributional impact of policies at the regional or local level. Spatial microsimulation models offer a geo-​referenced alternative to aggregate models. Spatial microsimulation models can therefore be used to explore spatial relationships and to examine the spatial impact of different policy scenarios at the national, regional, and subnational level. Early microsimulation modeling, particularly spatially referenced models demanded years of effort by large teams of researchers. This burden of making and maintaining these models discouraged many researchers from considering microsimulation methods (Anderson & Hicks, 2011). However, with the exponential increase in both computing power and data over the last two decades (Anderson & Hicks, 2011), microsimulation has become commonplace in demography, public health, social insurance, traffic analysis, sociology, geography and in all aspects of public policy (Ballas et al., 2007; Anderson & Hicks, 2011). However, when deciding on which procedure to employ two main factors are important. The algorithm must be able to process a combination of individual and household constraints and have adequate run-​time efficiency. To date a number of techniques have been developed to produce spatial microsimulation models (Voas & Williamson, 2001; Ballas et al., 2007; Farrell et al., 2013). The principle methods are sampling methods such as Iterative Proportional Fitting (IPF) and various combinatorial optimization (CO) methodologies (Williamson and Voas, 2001; Morrissey et al., 2008). The main difference between CO techniques and IPF is that IPF techniques use estimated joint-​probability distributions to create synthetic data at the small area level. In comparison, CO techniques retain the distributional interdependencies for each variable from the original dataset (Morrissey et al., 2008) and thus increase the rates of internal multivariate consistency between the original and simulated datasets. CO techniques may be either deterministic or probabilistic in nature. Deterministic reweighting assigns weights to each household based on the probability of that household belonging to the region in question (Ballas et al., 2005). Similar to IPF, deterministic reweighting algorithms are computationally efficient. Such algorithms are unsuitable for SMILE, however, because it has different units of analysis (individuals and households) which then require the use of non-​ trivial methods of weight generation (Farrell et al., 2013), such as generalized regression weight based methods (GREGWT) (Bell, 2000). GREGWT, developed by the Australian Bureau of Statistics (Bell, 2000) is a constrained distance minimization function which uses a generalized regression technique to get an initial weight and iterates the regression until an optimal set of household or individual weights for each small area is derived. However, Williamson (2009) highlights that when there are large numbers of constraints, the GREGWT does not always converge (Farrell et al., 2013).

 



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The alternative to deterministic reweighting is probabilistic reweighting processes, the most popular of which is Simulated Annealing (SA). SA allows the survey data and constraints to have different units of analysis. Unlike IPF, SA contains mechanisms to avoid becoming trapped at local minima (Wu & Wang, 1998). It is also less sensitive to convergence issues. Morrissey et al. (2008) provide a full discussion of the SA technique used to create the SMILE 2002 dataset. Williamson (2009) found that in an Australian simulation, SA performed slightly better at matching than GREGWT for both constrained and unconstrained variables. This was particularly the case in districts where there was no convergence. 8.3  SIMULATION MODEL OF THE IRISH LOCAL ECONOMY (SMILE) SMILE (Simulation Model of the Irish Local Economy) is a static spatial microsimulation model (Ballas et al., 2006a, b; Morrissey et al., 2008). SMILE 2002, based on the 2002 SAPS and the Living in Ireland Survey (2001) used SA to create a large geo-​referenced dataset capable of examining a host of policy specific areas including health (Morrissey et al., 2008, 2010, 2013), agri-​environment (Ballas et al., 2006a, b; Hynes et al., 2009a, b) and labor force participation (Morrissey & O’Donoghue, 2011). Cullinan (2010) further developed the model to include point locations for households. For a broader overview of how SMILE was used to examine each of these policy areas, please see Morrissey et al. (2013). However, while SA allows one to model both individual and household processes, the algorithm requires significant computational intensity due to the degree to which new household combinations are tested for an improvement in fit during simulation (Farrell et al., 2013). To illustrate, creating the SMILE agri-​environmental data, Hynes et al. (2009a, b) found that it took two days to generate almost 140,000 individual farm records from 1,200 survey data points on a 2G workstation. As a result, to create SMILE 2006 and match the Small Area Population Statistics (SAPS, 2006), SILC (2005) and POWCAR (2006) datasets a more computationally efficient method known as quota sampling was developed by Farrell et al., (2013). This process will be outlined below; however for an in-​depth outline of the process please see Farrell et al. (2013). Quota Sampling (QS) The QS methodology is based on probabilistic reweighting techniques (Ballas et al., 2007). Similar to the process of SA (Morrissey et al., 2008) survey

 

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data are reweighted according to key constraining totals, or ‘quotas’, for each local area. For both SMILE 2002 and 2006 these quotas are provided by the SAPS dataset. Five matching constraints were used in developing SMILE 2006; these include the number of individuals in each ED, the number of households in each ED, the number of individuals in each household, a tabulated age, sex variable and education level. In SMILE, the unit of analysis consists of individuals grouped into households, while the constraints can be either at the individual or household level. One of the key goals of the QS method is to achieve computational efficiency. To achieve this efficiency the QS process is apportioned into a number of iterations based on an ordered repeated sampling procedure (Farrell et al., 2013). The basic selection process operates as follows: • Households that comply with concurrent quota counts (taken from the SAPS dataset) are extracted from the microdata (SILC) population. • These observations are sorted randomly and assessed in order of convenience. • A household is selected as a resident of an ED if their demographic profile is the same as the constraining totals for an ED. To improve efficiency, this procedure considers both individual and multiple households in one simulation iteration. The candidate sample at each stage is limited to households eligible according to the quota counts at the start of the simulation. If a number of households are chosen such that the total population assigned is less than or equal to the smallest constraining quota the maximum number of households is assigned in one iteration. This ensures that quota counts are not exceeded, regardless of the distribution of characteristics. Thus, if the preliminary selection complies with concurrent quotas, it is admitted to the small area sample. Quota counts indicating the numbers of individuals for each constraining quota criterion are then amended, reduced by the sum of the matching characteristics of the assigned household(s). For individual level constraints, the running totals per constraint are incremented by the number of people in the household with that particular constraint. For household level constraints, it is incremented by 1. This process is repeated until all the small area defined quotas are reached. Once each count reaches its target quota, all households with the same demographic structure are removed from the sample. As noted by Farrell et al. (2013) this aids efficiency in the selection process as the population size reduces as the simulation progresses. If the small area population is larger than the survey data, the microdata is duplicated in an effort to achieve the EDs population size. In practice a number of issues arise with the successful implementation of QS. These include a bias towards sampling smaller households, an inability to

 



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adequately simulate certain demographic groups, due to disparities between survey and census data distributions and difficulties in allocating the final few households due to the increasingly restrictive nature of quota counts as the simulation progresses. To overcome these issues an ordered constraint procedure whereby difficult to allocate groups in the microdata, particularly large households and households containing children, are selected first (Farrell et al., 2013). Best practice in applying the ordered constraint process was determined through a series of sensitivity analyses, which tested every possible order of constraint configuration. The sensitivity analysis found that the most effective sampling configuration varied according to whether 2002 or 2006 micro data was used. Following this sensitivity analysis, the configuration of the first step used in this paper involved sampling households containing children only in the presence of a household-​size constraint and the other primary constraints of age, sex, and education. Once all households with children were successfully allocated, the children-​only constraint was then removed and this procedure was carried out in the presence of the primary constraints of age, sex education and a household-​size constraint. Following this step, the sampling procedure admits under-​represented groups, with the final step sampling from all eligible households. Carrying out this sampling procedure creates a spatially representative dataset. Finally, to overcome prohibitively restrictive quota counts, a process similar to the swapping of households in simulated annealing is required (See Morrissey et al., 2008). This process is done by removing constraints one by one until the quota is met. Constraints are removed in reverse order of the degree to which they influence household income. This is determined by pre-​ synthesis regression analysis. This design minimizes subjectivity, whereby the broadening of constraints is only introduced when absolutely necessary and in a fashion that ensures that those variables that explain the greatest level of variability are retained to the greatest extent. Generally all quotas are filled and this stage is skipped. As noted by Farrell et al. (2013) ordering the constraints in such a manner may cause validation issues to arise, in that the distribution for larger households or under-​represented groups may be less robust. To ensure this does not occur, validation of the QS output is an integral component of the model’s construction. The next section outlines the validation methods used within the SMILE model. Calibration The computation cost of QS and other methods of generating small area data limit the number of constraints one can use (Morrissey & O’Donoghue, 2011; Farrell et al., 2013). However the spatial heterogeneity of the simulated data depends upon the multivariate relationship of the matching constraints with

 

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nonconstraining variables. The need to optimize computational efficiency, while ensuring the spatial heterogeneity of the simulated dataset means that a calibration mechanism must be used (Morrissey et al., 2013; Morrissey & O’Donoghue, 2011). The purpose of the calibration procedure is to align the small area level data within SMILE with exogenous data on labor force participation and income. The procedure operates in two stages. The first stage estimates a set of equations (logistic or multinomial) determining the presence of an income based on labor force participation. The second step involves predicting the level of income for individual using logged income regression models. A full description and application of the calibration method in terms of labor force and income distributions and socio-​economic characteristics and health service utilization is provided by Morrissey and O’Donoghue (2011) and Morrissey et al., (2013), respectively. Using a probabilistic alignment technique the spatial distribution of unconstrained labor market characteristics are calibrated against SAPS totals. Once the correct distribution of these variables has been established, the level of income is calibrated according to external county level national accounts data (CSO, 2006). Definitional differences between micro level and national accounts data prohibit calibrating income in absolute terms, as scaling average income by source to the national accounts total can affect the distributional properties of the data. Thus, the calibration procedure is augmented in a step-​wise fashion to ensure average county income by income source (i.e., market income, social welfare income, capital income, etc.) corresponds to county level national accounts. This allows the same distribution properties of the underlying income data to be largely maintained. SMILE Marine The SMILE 2006 model thus far contains a fully calibrated residential distribution of employees and their income by industry for 2006. However, in order to model the local impact of the marine sector, the employment location of workers in the sector is required. To find both the residential and employment location of workers the marine dataset must be linked to the newly created SMILE dataset. Although it is impossible to identify specific business in the SMILE model, one may assume that the residential distribution of each employee within a wider industrial classification is the same for the relevant marine subsector. Sampling the number of workers from the marine sub-​sector, as outlined above, the residential distribution of these marine employees may be obtained at the local level. Using spatial microsimulation techniques, this research provides the first employment and income distribution of the marine at the local level. Using this newly created data, the next section presents the employment contribution and income contribution of the

 



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marine sector relative to the nonmarine sector at the county level. For the purpose of this chapter county refers to administrative boundary of which there are thirty-​four in Ireland. Section 8.4 outlines the data used to create SMILE marine. 8.4  DATA In order to examine the welfare impact of the marine sector a dataset consisting of three pieces of geo-​referenced information are required; employer output, employment and income data, employees’ residential location by industrial classification and individual income data. The SEMRU database collected and collated by Morrissey (2010) and fully outlined in Morrissey et al. (2011) contains output, employment and income data for each marine sector business in Ireland at varying spatial scales. However, this is a firm level dataset and does not contain the residential location of employees within each company. Residential data is required for welfare analysis as one is ultimately interested in the distribution of income earned by employees rather than the income generated by firms. Thus, a microlevel dataset containing employee residential and employment location by industrial classification is required. One potential dataset that contains employment by industrial sector data at a local scale is the Place of Work Census of Anonymised Records (POWCAR). POWCAR contains data from the 2006 Census for all individuals over fifteen years of age who were working for payment or profit. POWCAR includes both place of work and place of residence data at the small area, electoral district (ED) level. The industry categories contained in POWCAR, consistent with the Census industrial codes, are at a more aggregate level than the marine industry contained in the marine company dataset. The first stage therefore involves creating a new industry variable within the marine dataset based on the POWCAR industry variable. Table 8.1 presents the newly created marine industry variable. The POWCAR dataset contains the number of workers and nonworkers by industry and a variety of demographic and socio-​economic data, such as age, marital status, and socio-​economic status. However, it does not contain income information. In contrast, household survey data such as the Survey of Income and Living Conditions (SILC) contain income and employment information at the individual and household level. The SILC is a nationally representative longitudinal survey that began in 2003. In 2005 the SILC dataset contained 15,885 individuals. The dataset contains a variety of demographic and socio-​ economic characteristics, including income, employment and household composition statistics. However, while the SILC dataset contains employee and income data at the micro level this data is only available at a

 

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Table 8.1  Industrial Sectors Marine sector

POWCAR sector

Sea-​fisheries Aquaculture Tourism Other Services Commerce Renewable Energy Processing Oil & Gas Manufacturing Seaweed & Biotech

Agriculture, Forestry and Fisheries Agriculture, Forestry and Fisheries Commerce Commerce Commerce Commerce Manufacturing Industry Manufacturing Industry Manufacturing Industry Manufacturing Industry

coarse spatial scale—​the NUTS2 regional variable (containing two regions). As such, any analysis using the SILC survey is constrained to the national level. Using a matching algorithm to link the data in the SILC with the small area level POWCAR data and the marine dataset, a much richer dataset would be obtained that would allow an examination of the employment and welfare impact of the marine sector at the local level. If each of these datasets were linked, one would be able to examine the impact of marine sector at the place of work (output and employment) and place of residence (household welfare) across a consistent spatial scale. One can use spatial microsimulation techniques to accomplish this. 8.5  RESULTS Contrasting the outputs from a macro level Input-​Output (IO) and a spatial microsimulation model this chapter highlights the benefit of using both modeling techniques to develop a holistic overview of the impact of a sector at the national, county and small area and household level. National-​Level Analysis Policymakers are frequently preoccupied with the production and employment-​ creating effects of industrial expansion. Using the IO model produced by Morrissey and O’Donoghue (2012) and outlined in ­chapter  2, table 8.2 provides production and employment multipliers for the marine economy. From table 8.2, one can see that an €1 investment in water construction has the largest impact on the Irish economy and generates €1.01 additional spending in the economy. Marine auxiliary transport services have the lowest (€0.39). Combining the multipliers for each of the ten sectors, an additional €1 investment in the sector as a whole would generate €5.80 additional spending in the

 



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Table 8.2  Marine Production and Employment Multipliers Sector Fishing Oil & Gas Seafood Processing Boat Building Water Construction Water Transport Auxiliary Marine Transport Services Marine Engineering Marine Retail Water Based Activities Total Marine Sector

Production multipliers

Employment multipliers

0.57 0.43 0.74 0.73 1.06 0.58 0.39 0.68 0.62 0.51 5.08

0 0.50 0.43 0.17 0.95 0.14 0.12 0.19 0.15 0.18 2.9

wider Irish economy. Taking the ten marine sectors together, the total impact of the marine sector on employment is 2.9 (water construction, 0.9; oil and gas extraction, 0.5; seafood processing 0.4; marine engineering 0.2; WBA, 0.2; boat building, 0.2; fishing 0.2; retail, 0.1; water transport services, 0.1; marine auxiliary transport services, 0.1). That is, for every €100,000 invested in the marine sector as a whole approximately 3 individuals, FTE, will be employed. The analysis produced by the IO table provides important information for policymakers, particularly around investment options for different sectors. However, these results are limited to the national level. They do not indicate where these jobs will be located and what sections of the population, in terms of demographic and welfare characteristics, will gain from marine investment. The marine sector is specifically believed to be of high employment benefit to local and coastal communities (Morrissey, 2015), particularly within the marine resource sectors, fishing, aquaculture and seafood processing (Morrissey & O’Donoghue, 2012). To examine the microlevel impact of the marine sector, a small area individual level data is required. Using the outputs from a spatial microsimulation model, the following sections examine marine based income and employment at the local county, small area and household level. Marine Employment Contribution at the County Level Table 8.3 reports the county employment rate, the percentage of marine employment by county and total marine employment as a percentage of total county employment and the coastal status of the county. From table 8.3, it can be seen that although the marine sector is relatively small sector nationally (it represented 1 percent of GVA in 2007), it is of relatively strong importance in counties of the North and West (Mayo, Donegal, Kerry, and Galway) of Ireland. This is a function of both the coastal location of these counties and

 

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Table 8.3  Share of Marine Employment Per County

County

Region

Donegal Sligo Louth Leitrim Cavan Monaghan South Dublin Fingal Dublin City Dun Laoighaire Wicklow Kildare Meath Longford Westmeath Laois Offaly Clare Limerick Tipperary NR Wexford Waterford Tipperary SR Carlow Kilkenny Kerry Cork Mayo Galway Roscommon Total

Border Border Border Border Border Border Dublin Dublin Dublin Dublin East East East Midlands Midlands Midlands Midlands Mid-​West Mid-​West Mid-​West South East South East South East South East South East South West South West West West West

County employment rate 47.8 51.7 54 50.2 52.4 53.2 57.5 64 56.5 52.6 53.5 61.8 60.3 50.4 54.8 54.8 53.7 54.3 52.2 52.1 52.1 51.9 51 53.1 54.1 50.5 53.7 48.8 52.7 49.9

Overall % of marine employment 12.6 2.5 2 0.3 0.3 0.2 3.1 3.1 6.3 1.9 2.4 1.3 1 1.2 0.4 0.2 0.2 4 1.1 0.1 5.4 3.5 0.7 0.2 0.3 7.5 17.4 9.3 10.9 0.5 100

County % of employment by marine sector

Coast

3.6 1.5 0.7 0.4 0.2 0.2 0.5 0.4 0.4 0.4 0.7 0.2 0.2 1.4 0.2 0.1 0.1 1.3 0.2 0.1 1.6 1.2 0.3 0.2 0.1 2 1.3 2.9 1.7 0.3

1 1 1 0 0 0 0 1 1 1 1 0 0 0 0 0 0 1 0 0 1 1 0 0 0 1 1 1 1 0

Source: SMILE-​Marine

the relative peripherality and employment profile of these counties relative to the East Coast (Morrissey et al., 2011). Although much of the marine sector is in the non-​traditional marine sectors, particularly the marine service sector (Morrissey et al., 2011) and thus not necessarily tied to the coastline the vast majority of employment is within coastal counties. This allows companies to build upon the scale, knowledge spillover and agglomeration effects of existing marine businesses (Morrissey & O’Donoghue, 2012). From table 8.3 one can see that the majority of marine sector employment occurs in the counties

 



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with a high absolute employment share in the sector. Cork however is the main exception, having a mid-​ranked employment share from marine activities but having the highest proportion of total marine employment due to its larger size. Marine Income Contribution at the County Level Table 8.4 presents the average marine and non-​marine sector contribution to employment income by county. It can be seen that the average marine sector income contribution (€34,034) is higher than the non-​marine contribution (€30,153) in coastal counties. Table 8.4 also provides the marine versus nonmarine sector income ratio by county. In total it can be seen that the marine sectors have a positive income premium of on an average 12 percent compared to non-​marine sectors. Although the counties with the highest marine based employment rates (Donegal, Mayo, Kerry, and Galway) experience a slightly higher than average premium of between 10 and 15 percent, the highest premiums occur in non-​coastal counties such as Offaly, Cavan, and Tipperary North Riding. This is due to the low level of marine employment in noncoastal counties (0.69 percent compared to 6.39 percent in coastal counties) relative to average marine sector income as a whole. This is a further indication of the income premium in the marine sector. Examining the composition of marine income by subsector indicates that the average income across the sector is driven by subsector. Table 8.5 reports the ratio of average income by marine subsector relative to the national average. It can be seen from table 8.5 that the shipping sector has earnings below the national average, while in contrast commerce (1.28) and renewable energy sectors (1.14) have income averages significantly above the national average. However, both these sectors are non-​traditional marine sectors (Morrissey et al., 2011). The renewable energy sector is a high-​tech, research and development-​based sector, whilst the marine commerce sector comprises marine based financial services and insurance, with the majority of these companies located in urban coastal locations. The characteristics of these sectors within the wider Irish economy would indicate that employees would earn higher than average incomes. However, what is of interest is that traditional marine sectors such as fisheries (1.05) and aquaculture (1.01), which are predominately located in peripheral, coastal areas have a slightly higher than average income. Income Spread Mean income, however, only tells part of the story of the impact of a sector on the local economy. The spread of incomes is also important. A higher average with a lower spread indicates that the majority of individuals who work in the

 

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Table 8.4  Average Employment Income for Marine and Non-​Marine Sectors

County Cavan Donegal Leitrim Louth Monaghan Sligo Laois Longford Offaly Westmeath Galway Mayo Roscommon Kildare Meath Wicklow Clare Limerick Tipperary NR Carlow Kilkenny Tipperary SR Waterford Wexford Cork Kerry Dublin City South Dublin Fingal Dun Laoighaire County Average

Average nonmarine income per county 28446 25611 27265 32434 28203 28787 27663 28759 28016 28458 28906 28240 28393 32176 30178 33591 29208 31376 30347 25701 27613 30187 30608 28405 31144 27537 35740 35959 38124 37521 €30,153

Average marine income per county 35188 28090 23086 34696 39144 33771 25687 31916 47078 32677 32860 32167 30095 35505 27315 36349 34649 36382 50862 27783 31298 29407 34845 32346 33901 31441 37536 37049 39504 38814 €34,034

Ratio-​ marine/​ non marine 1.24 1.10 0.85 1.07 1.39 1.17 0.93 1.11 1.68 1.15 1.14 1.14 1.06 1.10 0.91 1.08 1.19 1.16 1.68 1.08 1.13 0.97 1.14 1.14 1.09 1.14 1.05 1.03 1.04 1.03

Coast 0 1 0 1 0 1 0 0 0 0 1 1 0 0 0 1 1 1 0 0 0 0 1 1 1 1 1 0 1 1

Source: SMILE-​Marine

sector have earnings above average. The spread of income may be measured using the I2 inequality statistics, which may be defined as:

I2 =

1  σ2  2  µ 2 

(8.1)

where σ2 is the standard deviation of income an μ2 represents average income.

 



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Table 8.5  Sub-​Sectoral Average Earnings (State = 1) Ratio average earnings relative to national average

Sector Non Marine Income

1.000

Marine Sub-​Sectors Shipping Tourism Other Services Sea-​fisheries Aquaculture Processing Oil & Gas Manufacturing Commerce Seaweed Renewable Energy

0.935 1.107 1.143 1.047 1.012 1.001 1.032 1.058 1.278 0.981 1.142

Table 8.6 indicates that of the twenty-​six counties with a higher average marine income compared to nonmarine, sixteen have a lower spread than the nonmarine sectors. This confirms that individuals who work in the marine sector in these counties have higher than average earnings. Ignoring Monaghan and Limerick, which have very few marine sector workers, the spread of earnings rises with the marine employment rate. The anomaly created by Monaghan and Limerick may be due to counties with relatively small sectors having lower spreads due to fewer workers. Another reason may be that these counties display specialization in specific high-​income marine sectors. Table 8.7 examines this hypothesis further and provides the marine subsector share of employment by county. From table 8.7 it can be seen that there is no obvious concentration of subsectoral activity in counties with smaller sectors. In these counties, there tends to be smaller concentrations of multiple sectors, rather than sectors building any specific concentration. As such, the strong income spread displayed in counties with small marine sectors, such as Monaghan and Limerick is due to fewer marine workers in these counties. 8.6  DISCUSSION As noted in the Introduction, the majority of economic modeling has historically taken place at the aggregate or mesolevel (Ballas et al., 2007). However, in order to formulate public policies, it is necessary not only to understand the nature and the operation of differing policies at the macro level but also

 

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Table 8.6  Earnings Inequality Per County

Cavan Donegal Leitrim Louth Monaghan Sligo Laois Longford Offaly Westmeath Galway Mayo Roscommon Kildare Meath Wicklow Clare Limerick Tipperary NR Carlow Kilkenny Tipperary SR Waterford Wexford Cork Kerry Dublin City South Dublin Fingal Dun Laoighaire

Nonmarine

Marine

Ratio

0.87 0.75 0.87 0.78 0.69 0.76 0.74 0.97 0.78 0.79 0.77 0.85 0.88 0.71 0.76 0.75 0.80 0.72 0.80 0.74 0.85 0.75 0.84 0.82 0.74 0.84 0.63 0.74 0.71 0.84

0.55 1.03 0.27 1.11 2.25 0.41 0.47 0.81 0.88 0.54 0.80 1.27 0.44 0.34 0.28 0.44 1.07 2.10 0.29 0.67 0.21 0.50 1.02 0.62 0.83 0.70 0.54 0.44 0.49 0.44

0.63 1.38 0.31 1.41 3.27 0.53 0.63 0.84 1.13 0.69 1.04 1.50 0.51 0.48 0.37 0.58 1.34 2.91 0.35 0.90 0.25 0.67 1.22 0.75 1.12 0.83 0.85 0.59 0.69 0.52

Source: SMILE-​Marine

to evaluate the likely impact of these policies on activity at the local level (Morrissey & O’Donoghue, 2012). Using a microlevel methodology, spatial microsimulation, the chapter provided marine-​based policymakers with the first local level analyses of the marine sector for an entire country. These results not only verify the importance of the sector to the local economy, but also add to the set of national and regional economic indicators recently developed—​outlined in ­chapters  2 through 7 of this book. To conclude, previous research on the marine sector, constrained by the lack of spatial referencing had been limited to the national and regional level. Using a spatial microsimulation model, this paper presented the first welfare analysis of the marine sector at the county and small area level. It was found

 



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Table 8.7  Percentage of Marine Employment by Subsector Marine Sectors Cavan Donegal Leitrim Louth Monaghan Sligo Laois Longford Offaly Westmeath Galway Mayo Roscommon Kildare Meath Wicklow Clare Limerick Tipperary NR Carlow Kilkenny Tipperary SR Waterford Wexford Cork Kerry Dublin City South Dublin Fingal Dun Laoighaire

1 6 9 0 18 8 0 24 1 4 6 2 0 2 24 21 33 9 40 18 10 32 4 24 13 14 0 36 31 33 46

2 8 21 18 21 0 67 14 1 19 13 37 16 11 24 23 45 72 18 35 18 42 9 42 48 31 60 33 38 35 33

3 40 1 16 4 43 3 41 4 15 18 1 1 11 15 14 4 2 10 6 28 0 9 1 2 4 2 5 5 4 2

4 15 19 12 16 0 4 0 1 12 10 9 5 6 3 7 6 4 5 0 3 4 7 18 20 13 12 5 3 4 5

5 10 10 2 17 8 8 0 0 42 6 12 8 9 5 0 1 5 1 0 13 0 7 7 3 7 7 0 0 0 0

6 2 30 6 19 10 1 3 1 0 0 8 13 12 7 17 3 3 11 12 5 4 5 3 9 17 14 5 5 6 4

7 2 0 4 0 3 7 0 1 0 1 1 46 12 0 0 0 0 2 0 0 0 1 0 0 1 0 0 0 0 0

8 15 8 37 2 30 3 3 92 8 37 22 9 28 7 6 6 2 8 29 13 12 58 3 2 9 2 8 9 10 5

9 0 0 2 0 0 0 3 1 0 6 6 1 7 4 9 1 4 2 0 8 4 0 2 2 1 0 5 3 3 2

10 2 0 0 0 0 0 0 0 0 1 0 0 0 4 1 0 0 0 0 0 0 0 0 0 0 0 2 3 4 1

11 0 2 4 0 0 8 0 0 0 0 1 1 1 0 1 0 1 3 0 3 2 0 0 1 1 3 0 2 0 0

12 0 0 0 4 0 0 14 0 0 0 0 0 0 6 1 0 0 1 0 3 0 0 0 0 1 0 1 2 2 1

Source: SMILE-​Marine 1 -​Sea-​fisheries; 2 -​Aqua-​culture; 3 -​Processing; 4 -​Oil & Gas; 5 -​Manufacturing; 6 -​Seaweed & Biotech; 7 -​Tourism; 8 -​Other Services; 9 -​Commerce; 10 -​Renewable Energy; 11 -​Shipping; 12 -​High Tech Services

that although the marine sector is a relatively small sector in terms of employment nationally, it is of relatively strong importance to counties in the North and West (Mayo, Donegal, Kerry, and Galway) of Ireland. BIBLIOGRAPHY Anderson, R., Hicks, C. (2011). Highlights of contemporary microsimulation. Social Science Computer Review 29(1): 3–​8.

 

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Ballas, D., Clarke, G. P. (2001). The local implications of major job transformations in the city: A spatial microsimulation approach. Geographical Analysis 33(4):  291–​311. Ballas, D., Clarke, G. P., Wiemers, E. (2006). Spatial microsimulation for rural policy analysis in Ireland: The implications of CAP reforms for the national spatial strategy. Journal of Rural Studies 22: 367–​378. Ballas, D., Clarke, G. P., Dewhurst, J. (2006). Modelling the socio-​economic impacts of major job loss or gain at the local level: a spatial microsimulation framework. Spatial Economic Analysis 1(1): 127–​146. Ballas, D., Clarke, G. P., Dorling, D., Rossiter, D. (2007). Using SimBritain to model the geographical impact of national government policies, Geographical Analysis 39(1):  44–​77. Ban, N. C., Bodtker, K. M., Nicolson, D., Robb, C. K., Royle, K., Short, C. (2013). Setting the stage for marine spatial planning: Ecological and social data collation and analyses in Canada’s Pacific waters. Marine Policy 39: 11–​20. Bell, P. (2000). GREGWT and TABLE macros –​Users guide, Canberra, Australian Bureau of Statistics, Unpublished. Birkin, M., & Clarke, G. (1989). The generation of individual and household incomes at the small area level using synthesis. Regional Studies 23(6): 535–​548. Braunerhjelm, P., Fanini, R., Norman, V., Ruane, F., Seabright, P. (2000) Integration and the Regions of Europe: How the Right Policies Can Prevent Polarization. Monitoring European Integration 10. London: Centre for Economic Policy Research. Central Statistics Office (2006) National Account 2006, Central Statistics Office, Dublin. Central Statistics Office (2013) County Incomes and Regional GDP, Central Statistics Office, Dublin. Cullinan, J. (2010). Developing a continuous space representation of a simulated population. Spatial Economic Analysis 5: 317–​338. Druckman, A., & Jackson, T. (2008). Household energy consumption in the UK: A highly geographically and socio-​economically disaggregated model, Energy Policy 36(8), 3177–​3192. Edwards, K., Cade, J., Ransley, J., & Clarke, G. (2009). A cross section study examining the pattern of childhood obesity in Leeds: Affluence is not protective. Archives of Diseases in Childhood 69: 1127–​1134. Farrell, N., Morrissey, K., O’Donoghue, C. (2013). Simulated Model for the Irish Local Economy. In: Edwards, K. and Tanton, R. (eds). Microsimulation Methods and Models. Springer, London. Gruber, S., Soci, A. (2010). Agglomeration, agriculture, and the perspective of the periphery. Spatial Economic Analysis 5(1): 43–​72. Gong, C., McNamara, J., Vidyattama, Y., Miranti, R., Tanton, R., Harding, A., & Kendig, H. (2011). Developing spatial microsimulation estimates of small area advantage and disadvantage among older Australians. Population, Space and Place 18(5):  551–​565. Harris, R., Moffet, J., Kravtsova, V. (2011). In Search of ‘W’. Spatial Economic Analysis: 6(3): 249–​270.

 



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Hynes S., Morrissey, K., O’Donoghue, C., Clarke, G. (2009a). A spatial microsimulation analysis of methane emissions from Irish agriculture. Ecological Complexity 6(2):  135–​146 Hynes, S., Morrissey, K., O’Donoghue, C., Clarke, G. (2009b). Building a static farm level spatial microsimulation model for rural development and agricultural policy analysis in Ireland. International Journal of Agricultural Resources, Governance, and Ecology 8(2): 282–​299. Kildow, J. T., McIlgorm, A. (2010). The importance of estimating the contribution of the oceans to national economies. Marine Policy 34: 367–​374. Krugman, P. (1980). Scale economies, product differentiation, and the pattern of trade. American Economic Review 70: 950–​959. Krugman, P. (1991) Increasing returns and economic geography. Journal of Political Economy 99: 483–​499. Krugman, P. R., Venables, A. J. (1995). Globalization and the inequality of nations. Quarterly Journal of Economics 110: 857–​880. Morrissey, K., Clarke, G., Ballas, D., Hynes, S., O’Donoghue, C. (2008). Analysing Access to GP Services in Rural Ireland using micro-​ level Analysis. Area 40(3):  354–​364. Morrissey K., Clarke, G., Hynes, S., O’Donoghue, C. (2010). Examining the factors associated with depression at the small area level in Ireland using spatial microsimulation techniques. Irish Geography 43(1): 1–​22. Morrissey, K. (2010). SEMRU Marine Database, SEMRU, J.E. Cairnes School of Economics and Business, NUI Galway. Morrissey, K. (2015). An inter and intra-​ regional exploration of the marine sector employment and deprivation in England, The Geographical Journal 181(3), 295–​303. Morrissey, K. O’Donoghue, C. (2011). The spatial distribution of labour force participation and market earning at the sub-​national level in Ireland. Review of Economic Analysis 3(1): 80–​101. Morrissey, K., O’Donoghue, C., Hynes, S. (2011). Quantifying the value of multi-​ sectoral marine commercial activity in Ireland. Marine Policy 35(5):, 721–​727. Morrissey, K., O’Donoghue, C. (2012). Marine economy and regional development. Marine Policy 36: 358–​364. Morrissey, K., O’Donoghue, C. (2013). The role of the marine sector in the Irish national economy: An input–​output analysis. Marine Policy 37: 230–​238. Morrissey, K., O’Donoghue, C., Clarke, G., Li, J. (2013). Using simulated data to examine the determinants of acute hospital demand at the small area level. Geographical Analysis 45(1): 49–​76. O’Mahony, C., Gault, J., Cummins, V., Kopke, K., O’Suilleabhain, D. (2009). Assessment of recreation activity and its application to integrated management and spatial planning for Cork Harbour, Ireland. Marine Policy 33: 930–​937. Orcutt, G. (1957). A new type of socio economic system, Review of Economics and Statistics 58: 773–​797. Perugini, C., & Martino, G. (2008). Income inequality within European regions: Determinants and effects on growth, Review of Income and Wealth 54(3):  373–​406.

 

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Pfeifer, C., Sonneveld, M. P., & Stoorvogel, J.J. (2012). Farmers' contribution to landscape services in the Netherlands under different rural development scenarios. Journal of Environmental Management 111: 96–​105. Rodríguez-​ Pose, A., & Tselios, V. (2009). Mapping Regional Personal Income Distribution in Western Europe: Income Per Capita and Inequality, Czech Journal of Economics and Finance 59(1): 41–​70. Rodríguez-​Rodríguez, D., Malak, D. A., Soukissian, T., Sánchez-​Espinosa, A. (2016). Achieving blue growth through maritime spatial planning: offshore wind energy optimization and biodiversity conservation in Spain. Marine Policy 73: 8–​14. Smith, D., Pearce, J., & Harland, K. (2011). Can a deterministic spatial microsimulation model provide reliable small-​area estimates of health behaviors? An example of smoking prevalence in New Zealand, Health and Place 17, 618–​624. Tanton, R., Vidyattama, Y., Nepal, B., MaNamara, J., Vu, Q., & Harding, A. (2009). Old, single and poor: Using microsimulation and microdata to analyse poverty and the impact of policy change among older Australians. Economic Papers 28(2):  102–​120. Tomintz, M., Clarke, G. P., Rigby, J. (2008). The Geography of Schooling in Leeds: Estimating individual smoking rates and the implications for the location of stop smoking services. Area 40(3): 341–​353. Voas, D., Williamson, P. (2001). Evaluating goodness-​of-​fit measures for synthetic microdata. Journal of Geographical and Environmental Modelling 5(2): 177–​200. Williamson, P. (2001). Modelling alternative domestic water demand scenarios, In: G. Clarke and M. Madden (eds.) Regional Science in Business, Berlin: Springer-​Verlag. Williamson, P. (2009). Simulating an ageing population: A microsimulation approach applied to Sweden. International Journal of Microsimulation,  66–​67. Williamson, P., & Voas, D. (2001). Evaluating goodness-​of-​fit measures for synthetic microdata, Journal of Geographical and Environmental Modelling 5(2): 177–​200. Wu, L., & Wang, Y. (1998). An Introduction to Simulated Annealing Algorithms for Computation of Economic Equilibrium. Computational Economics 12: 151–​169.

 

Chapter Nine

The Marine Sector A Panacea in Peripheral, Deprived Areas?

9.1  INTRODUCTION The fact that the human population is predominately concentrated in coastal regions around the world is taken as one of the primary reasons to mitigate sea level rise and climate variability. However, much of this population and therefore the subsequent policy is focused in large urban areas. In contrast, public policy has all but forgotten small coastal areas. Areas that in their own right are undergoing rapid economic and social changes (Urquhart & Acott, 2013; Van Putten et al., 2016). Internationally, only one policy area has systematically focused on small coastal areas. Much research and policy has focused on the economic and social implications of the decline in fisheries (Urquhart & Acott, 2013) in coastal areas (Van Putten et al., 2016). Within the UK and Ireland, attention has also been paid to the decline in traditional seaside resorts (Cawley & Clark, 2016), with a number of media articles demonstrating higher than average levels of deprivation experienced by seaside towns across Britain. However, as this book has demonstrated, the number of industries that comprise the marine sector is much larger than the fisheries or seaside tourism sectors. Sectors such as marine renewable energy, maritime transport and logistics, blue biotechnology and coastal protection have been highlighted as marine based sectors with high economic potential (Blue Growth, 2012). Within the marine tourism sector more broadly, increased interest in water-​based activities such as diving, surfing and sea-​kayaking have attracted a new generation to coastal areas. On the other hand, research in Ireland (Morrissey & O’Donoghue, 2012) and Surís-​Regueiro et al. 2014 found that marine and fishing activities respectfully, are equally important in terms of GDP and employment in large urban hubs as they are in peripheral, coastal regions. 145

 

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Chapter 8 introduced a small area based analysis of the marine economy for Ireland. To continue this local level analysis, this chapter will use a range of datasets, including both business and socio-​economic data understand the intraregional impact of the marine sector in England. Specifically, this chapter explores whether employment in the marine sector is located in areas with higher than average deprivation, addressing empirically the question “is marine activity an economic lifeline in deprived coastal areas”? Following research that has indicated that marine activity is not just based in coastal areas (Kildow & McIlgorm, 2010; Morrissey & O’Donoghue, 2012; Hynes & Farrelly, 2012), this chapter defines marine activity according to business based activity, what sectors use the marine resource as an input in their ­production cycle, rather than a geographical definition of marine activity. Section 9.2 outlines the data used for the purpose of this analysis. Section 9.3 uses the secondary dataset on business activity in England and geo-​referenced data on area level deprivation to examine whether the marine sector really is a panacea in peripheral, deprived areas. Section 9.4 offers a discussion of the results and Section 9.5 offers concluding remarks.

9.2  DATA Business Structure Dataset As outlined in ­chapter  3, the BSD is an annual snapshot of the Interdepartmental Business Register hosted by the Office of National Statistics (ONS) (Evans & Welpton, 2009). The BSD “snapshot” is taken every March and although the number of variables found in the BSD is small relative to other data sources, the BSD has extensive coverage, since any organization registered for VAT or PAYE is included (Evans & Welpton, 2009). As outlined in ­chapter  3 the BSD contains enterprise level data by SIC code (2003 and 2007) on key economic indicators for the marine sector; including turnover, employment (including owners), employees (excluding owners) and a number of enterprises and a spatial variable Government Office Region (GOR). Access to the BSD is obtainable via the virtual microdata laboratory hosted by the ONS in England and Wales (Evans & Welpton, 2009). Accessing the BSD via the virtual microdata laboratory and using the standard industrial classification (SIC) codes for 2007, eleven marine sectors were identified. Table 9.1 provides an overview of the SIC 2007 sectors identified as marine based and include; marine fishing, marine aquaculture, seafood processing, passenger sea and coastal transport, freight sea and coastal transport, renting and leasing of passenger transport, renting and leasing of freight transport, building and repairing of ships and boats,

 



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Table 9.1  Marine Subsectors within the Bis SIC 2007 Code

SIC 2007 Description

3110 3210 10200 50200 77341 77342 33150 30110 33200 30120 42910

Marine fishing Marine aquaculture Processing and preserving of fish, crustaceans and molluscs Sea and coastal freight water transport Renting and leasing of passenger water transport equipment Renting and leasing of freight water transport equipment Repair and maintenance of ships and boats Building of ships and floating structures Installation of industrial machinery and equipment Building of pleasure and sporting boats Construction of water projects

building of pleasure boats and water construction (for example, pier/​marina construction). The BSD did not provide information on a number of marine sector activities including coastal and marine tourism and marine engineering. Thus, the marine sector data described here reflects a trade-​off between comparability and precision. However, as outlined in ­chapter  3, whilst the non-​availability of marine and coastal tourism is a limitation to this research, primary data collection is time-​consuming and expensive (Morrissey et al., 2011). Due to these resource constraints Colgan (2013) argues that the “derivation of ocean economy estimates should be undertaken by adapting existing data to the purpose” (Colgan, 2013, p. 336). It is further important to note that this chapter uses the employment data on the marine sector contained in the BSD rather than turnover as employment rates represent the return to the community from a particular economic activity, rather than turnover, which may not be spent/​distributed within the area of interest. Turnover is also not representative of gross value added (profit; outputs minus inputs), so whilst one person enterprises such as sea fisheries in England may return their entire turnover to a specific area, turnover is not representative of their take home pay. The BSD does not contain data on firm level input costs, thus GVA for each firm cannot be calculated. Thus, this chapter uses employment in the marine sector as an indicator of socio-​ economic impact. Geographical References Within the BSD The BSD includes a regional geo reference at the GOR level for each business. Chapter 8 outlined the necessity to have data that allows the spatial impact of economic activity to be examined at a local level. Thus, of greater importance to this research is that the BSD also includes a “virtual” postcode

 

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for each business. Original postcodes are valuable fields for statistical use. However, they are directly recognizable identifiers and so are not allowed in the Virtual Microdata Laboratory. Thus, for datasets within the Virtual Microdata Laboratory postcodes have been replaced with alternatives, which maintain the exact statistical characteristics of the original variables. These variables are internally consistent within the Virtual Microdata Laboratory and have been linked to external higher geographies including Lower Super Output Area (LSOA). Linking the virtual postcodes created by the secure data setting team with externally consistent LSOA identifiers allows one to link the English Index of Deprivation (IMD) 2010 to the BSD. Socio-​Economic Data As noted by Westling et al. (2009) there is a wide range of socio-​economic data is available for England. The primary body responsible for collecting, analyzing and presenting socioeconomic data is the ONS for England and Wales. The most complete and significant socio-​economic dataset in the UK is derived from the UK Census, which counts all people and households within the UK every ten years. The data cover information about the population in terms of housing, health, employment, transport, and ethnic groups, and are provided at national, regional and local scale (ONS, 2011). However, the data required to conduct socio-​economic analysis requires a wide range of variables, such as crime, employment and health statistics. Such data can be derived from various UK governmental departments and local authorities and are updated on a more frequent basis than the census, often annually or every second year. However, this data are often not available at the same spatial and temporal scale as the UK Census data and is usually collected for different purposes, requiring a specific survey design (Westling et al., 2009). To overcome this problem, attempts have been made to combine different socio-​economic data from different sources into coherent datasets or indices and classifications. Of interest to this paper is the IMD. The IMD 2010 covers a wide range of socio-​economic variables, and serves as the basis for exploring the socio-​economic characteristics of a population in this paper. Index of Multiple Deprivation for England 2010 The Social Disadvantage Research Centre at the University of Oxford constructed the IMD on behalf of the Department of Communities and Local Government (DCLG, 2011). The IMD is partially based on Census data, but uses a combination of Census data and data derived from other sources such as the Inland Revenue, the Department of Health and the Department of Transport. The purpose of the IMD is to measure multiple deprivation at the

 



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small area level to identify the most disadvantaged areas in England (Noble, 2004). The IMD 2010 was constructed by combining seven general welfare domain scores weighted as followed; income (22.5 percent), employment (22.5 percent), housing and disability (13.5 percent), education, skills and training (13.5 percent), barriers to housing and services (9.3 percent), crime (9.3 percent), and living environment (9.3 percent). The IMD is based on data at the LSOA, which are designed to be consistent in population size. On an average, each LSOA contains 1,500 people (Westling et al., 2009). The IMD is available in two forms. As a rank variable that shows how an individual LSOA compares to other LSOAs in the country, and secondly as an absolute score (Noble, 2004). This paper is specifically interested in each LSOAs absolute deprivation score. The LSOA ranked number one is the most deprived, with higher rankings indicating less deprived areas. Linking the IMD for 2010 to the data on firm level activity within the BSD this paper is the first empirical research that uses the BSD to examine the potential socio-​ economic impacts of a specific sector. 9.3  RESULTS Linking the BSD, with data from the IMD for England 2010 at the LSOA area level this chapter examines whether marine sector employment is concentrated in areas of high deprivation. This chapter is particularly interested in whether employment in the seafood industry (marine fishing, aquaculture and seafood processing) is located in areas of high deprivation. To begin the analysis, table 9.2 presents the national and regional employment and turnover for the English marine sector as defined in table 9.1. Table 9.2 indicates that employment in the marine sector is approximately 72,000 full time equivalents and the sector generated £12.9 billion in turnover in 2010. Table 9.2 also presents the regional breakdown of marine sector activity. Table 9.2 indicates that in terms of employment the South East of England has the highest percentage of overall marine employment (31 percent). With regard to turnover, table 9.2 finds that both the South East and Greater London generate 29 percent of regional marine turnover. Whilst this paper focuses on levels of employment as an indicator of socio-​economic impact, it is interesting to note that turnover for the marine sector in the Greater London area is relatively high. Similar to work in Ireland by Morrissey and O’Donoghue (2012) this indicates that marine sector activity is not located in peripheral regions but is also represented in core or national economic centers. Running an inter-​and importantly intra-​regional analysis, table 9.3 presents the average regional deprivation score and the overall regional rates of employment for all businesses in the BSD, marine businesses, fishing,

 

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Table 9.2  Employment and Turnover in the Marine Sector at the National And Regional Level Region

Employment

North East North West Yorkshire & Humber East Midlands West Midlands East England Greater London South East England South West England National

3,229 4,107 5,244 3,933 1,324 8,483 7,614 22,600 15,692 72,226

Regional Percentage 4% 6% 7% 5% 2% 12% 11% 31% 22%

Turnover £ 368,093 840,872 911,533 359,016 186,932 1,286,442 3,678,551 3,716,520 1,585,625 £12,900,000

Regional Percentage 3% 7% 7% 3% 1% 10% 29% 29% 12%

Table 9.3  Average Lsoa Deprivation Score by Region and at the National Level and the Percentage of Employment across Each Region for the English Economy as a Whole, the Marine Sector and the Seafood Sector Region North East North West Yorkshire & Humber East Midlands West Midlands East England Greater London South East England South West England National Average

IMD Score All

IMD Score Marine

IMD Score Fishing

IMD Score Aquaculture

IMD Score Processing

25.6 (4%) 25.5 (10%) 23.2 (8%)

25.4 (4%) 24.6 (6%) 32.2 (7%)

24.15 (8%) 25.16 (6%) 31.35 (12%)

N/​A 15.3 (21) 15.8 (7%)

27.6 (2%) 31.8 (23%) 50.4 (41%)

19.6 22.1 14.6 22.9

17.1 18.2 18.3 21.1

(5%) (2%) (12%) (10%)

21.76 (2%) 19.9 (1%) 17.84 (10%) 19.92 (>1%)

19 (6%) 11.7 (3%) 15.3 (11%) 24.8 (1%)

16.9 22.1 24.4 29.3

13.4 (17%)

15.8 (31%)

18.50 (18%)

10.7 (17%)

11.8 (7%)

16.7 (9%)

19.6 (21%)

20.6 (40%)

14 (38%)

23.7 (17%)

19.5

20.13

21.68

14

31.25

(8%) (9%) (13%) (21%)

(3%) (>1%) (2%) (7%)

aquaculture and seafood processing business. Table 9.3 also presents the national average deprivation score for each of these five categories of economic activity. The maximum deprivation score within the BSD is 87.7. Table 9.3 shows that the average LSOA deprivation score at the national level for every business represented in the BSD is 19.5. Table 9.3 shows that the Greater London region has the highest regional employment rates in the England. On average this employment is located in LSOA with a deprivation

 



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score of 22.9. The South East Region, which has the lowest average deprivation score (13.4) for England has 17 percent of overall employment. This is in contrast to the North East region, which employs the lowest percentage of individuals regionally, 3 percent and this employment is located in areas with the highest average deprivation score (25.5) of the nine regions. Table 9.3 indicates that rates of employment are lowest in regions with high average deprivation (the North East) and highest in areas with low deprivation (South East) or in around the capital city, London. However, it is interesting to note, that employment in London is located in areas with relatively high deprivation (fourth highest in table 9.3), particularly compared to the two other regions (the South East and South West) classified as the South of England. With regard to employment in the marine sector as a whole, marine employment in Yorkshire and Humber are located in areas with the highest deprivation score (32.2) and these businesses represent 7 percent of businesses in the marine sector in England. The South East (31 percent) and the South West (21 percent) have the highest level of marine employment at the regional level. The marine businesses in these two regions are located in areas below the average deprivation score for marine businesses as a whole (20.13). However, comparing the average regional deprivation score for marine employment in the South East and the South West, these businesses are located in LSOA that are above the national average deprivation score for English businesses as a whole (19.5). Thus, whilst marine employment may be located in the least deprived regions in England, within regions this employment is located in areas that have higher than average deprivation scores compared to the national average (19.5, column 2, table 9.3). Fishing Sector Examining the fishing sector, table 9.3 shows that across the nine regions, fishing employment is located in areas with an average deprivation score of 21.68. Fishing employment is located in areas with higher than average deprivation scores than employment across the English economy (19.5) and across the wider marine sector (20.13). As with marine businesses as a whole, employment in fishing in the Yorkshire and Humber region are located in areas with the highest average deprivation score, 31.35. Employment in this region represents 12 percent of the English fishing sector. In contrast, 40 percent of fishing employment is located in the South West region, which has the second lowest inter-​regional deprivation score (20.6) across all fishing businesses. However, whilst the fishing employment located in the South West region is located in areas below the regional average deprivation score for fishing businesses as a whole (21.68), these businesses are located in areas above the national deprivation average for businesses as a whole (19.5). Thus,

 

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examining fishing employment inter-​regionally indicates that employment in the sector has a larger representation in deprived areas as a whole. Table 9.3 indicates that across the nine regions, aquaculture employment is located in areas with an average deprivation score of 14. This is the lowest deprivation score for each of the five sectors represented in this paper. Aquaculture employment is therefore located in areas with lower than average deprivation than employment across the whole English economy (19.5) and employment across the marine economy as a whole (20.13). These results are in contrast to fishing activity. Examining the aquaculture sector, the highest average deprivation score for aquaculture based businesses (29.34) are located in the Greater London region. However, it is important to note that these businesses only represent 1 percent of the English aquaculture sector. In contrast, the South East and the South West of England with average LSOA deprivation scores at the regional level, 10.7 and 14, respectively, both represent 17 percent and 38 percent of aquaculture employment in England. Examining employment within regions one can see that the aquaculture businesses are also located in regions below the average deprivation score for aquaculture businesses as whole (14). However, unlike the marine and fishing sectors, aquaculture based businesses are also located in areas below the national average (19.5). This analysis indicates that aquaculture employment is not necessarily located within regions with high levels of LSOA based deprivation or in areas within regions with higher than average deprivation. With regard to the processing sector, table 9.3 indicates that across the nine regions, seafood processing employment is located in areas with an average deprivation score of 31.25. Seafood processing businesses are therefore located in areas with much higher than average deprivation than businesses across the whole English economy (19.5) and businesses across the whole marine economy (20.13). Indeed, seafood processing employment is located in areas with the highest deprivation scores within this analysis. Table 9.3 shows that the highest percentage of processing employment is located in the Yorkshire and Humber area (41 percent), in areas with an average deprivation score of 50.4. Comparing the average deprivation score for the processing sector (31.25) to that of Yorkshire and Humber (50.4), one can see that seafood processing employment in this region is predominantly located in areas with much higher than average LSOA deprivation scores. Thus, table 9.3 indicates the importance of the processing industry to deprived areas in Yorkshire and Humber. This analysis provides the first empirical evidence that employment in marine based business, particularly fishing and seafood processing are located in LSOAs with higher than average deprivation, even if the regional rate of deprivation is relatively low (the South East and the South West). However, interestingly this is not true for the aquaculture

 



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sector. This analysis indicates that while regional level analysis does provide subnational evidence of the relationship between deprivation and marine sector employment, intraregional analysis provides more insight on the socio-​ economic impact of the marine sector. 9.4  DISCUSSION Human activities in the world’s oceans and coasts are at an unprecedented scale and expanding rapidly (Stojanovic & Farmer, 2013). The oceans have become a focal point for many new activities including wind and wave power, marine biotechnology, marine technology and other enterprises (Kildow & McIlgorm, 2010; Morrissey et al., 2011). In recognizing the multifaceted nature of the marine sector, governments and supranational organizations have begun to realize the economic potential of the marine resource (Morrissey et al., 2011, European Commission, 2012). Coastal and marine policymakers and managers are increasingly aware of the need to support and analyze the economic and social dimension of marine activity. Indeed, one of the EU’s Integrated Maritime Policy (IMP) initial aims is to begin to understand and appreciate the interlinkages that exist between different domains and functions of the marine resource (European Commission, 2012). Geographically, the marine sector has traditionally been seen as being part of the peripheral economy; located in coastal areas characterized by low levels of employment and production opportunities and high levels of deprivation. Indeed, recent research in the UK has shown that coastal areas have higher than average deprivation (House of Commons, Communities and Local Government Committee, 2007). However, research has found that marine based activities are not solely based in coastal areas (Morrissey & O’Donoghue, 2012) and that the coastal economy and the marine economy are not synonymous (Hynes & Farrelly, 2012; Kildow & McIlgorm, 2010; Colgan, 1997). Indeed, similar to the research presented in ­chapter  4 for Ireland, this analysis also found that 29 percent of marine based turnover is generated in the England’s core economic region, the Greater London region. However, the small area analysis presented here found that the marine sector, particularly fishing and seafood processing are indeed located in areas with higher than average deprivation, even if the overall regional rate of deprivation is relatively low (for example, the South East and the South West). In contrast, it was further found that employment in aquaculture is located in less deprived areas. Thus, whilst it is untrue that marine sector activity is a lifeline specifically in coastal areas, the sector is an important source of employment in deprived areas.

 

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Receiving substantial political and media attention the “plight” of UK coastal areas (The Guardian, 2013) has meant that marine based activities are seen as an important instrument for channeling national and supranational funding to these areas. In response, the newly implemented European Maritime and fisheries fund (EMFF) under the revised Common Fisheries Policy (EU CFP, 2009), has been designed as a financial instrument to aid in the sustainable development of coastal areas through fisheries, aquaculture and maritime activity across the EU. Thus, in terms of political and economic ramifications of this chapter’s findings, it is important that measures to aid coastal areas are not only filtered through marine policies as certain coastal areas may not have a marine sector and vice versa the marine activity may not be located in coastal areas. However, as a means of offering economic opportunity in deprived areas as a whole, marine activities as a core economic activity, offer employment opportunities in areas with above average national and region deprivation. Finally from a methodological perspective, to evaluate the impact of funds such as the EMFF and the IMP and to develop a deeper understanding of the economic and social impacts of the marine sector on coastal areas, policymakers require methods and techniques capable of estimating the impact of the sector across various spatial levels (Morrissey et al., 2014; Stojanovic et al., 2010). 9.5  CONCLUSIONS To date the majority of industry based economic analysis has taken place at the aggregate, national or regional level (Ballas et al., 2007) using macro level indicators. This is particularly true for the marine sector, where data limitations and the multifaceted nature of the sector (Colgan, 1997) have hampered economic and social assessments in general. This chapter describes the first attempt to develop a socio-​economic subregional analysis of the marine sector across both coastal and noncoastal areas. Linking spatially referenced firm level data and small area level (LSOA) data on deprivation with the BSD, this chapter found that not only is the marine sector within important with poorer regions, but also that the sector is of importance in deprived areas within relatively affluent regions. This analysis further highlights that regional averages for deprivation and economic indicators such as employment are not representative of intraregional economic activity and its impact on the welfare of localities and individuals. Furthermore, given the increasing emphasis on sustainable planning frameworks and socio-​economic based assessments under the Marine Strategy Framework in Europe (O’Mahony et al., 2009) this paper presents a range of accessible secondary data (Colgan,

 



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2013) that policymakers and practitioners may use to examine the local impacts of marine-​based policies. With regard to the marine activity within this chapter, the BSD did not provide information on a number of marine sector activities (e.g., coastal and marine tourism, marine engineering, etc.) that are considered in other international estimates of the marine sector (Morrissey et al., 2011; Kildow & McIlgorm, 2010). However, as outlined in ­chapter  3, the marine sector data described here reflects a trade-​off between comparability and precision. However, the aim of this paper was also to demonstrate how secondary data, at varying spatial scales may be used to examine the socio-​economic impacts associated with the marine sector, rather than a precise definition based overview of the sector.

BIBLIOGRAPHY Ballas, D., Clarke, G.P., Dorling, D., Rossiter, D. (2007). Using SimBritain to model the geographical impact of national government policies. Geographical Analysis 39(1):  44–​77. Cawley, M., Clark, G. (2016). Perspectives from the United Kingdom and Ireland. Tourism, Recreation and Regional Development: Perspectives from France and Abroad, 225. Colgan, C. S. (1997). Estimating the value of the ocean in a national income accounting framework, preliminary estimates of gross product originating for 1997. National Ocean Economics Project, Working Paper 1. Colgan, C. (2013). The ocean economy of the United States: Measurement, distribution and trends. Ocean and Coastal Management 71: 334–​343. Cicin-​ Sain, B., Knecht, R. W. (1998). Integrated Coastal and Ocean Management: Concepts and Practices. Island Press, Washington DC. European Common Fisheries Policy (EU CFP) (2009). Green paper: reform of the common fisheries policy. COM (2009) 163 final. Brussels. CEC. Department of Communities and Local Government (DCLG) (2012). The english indices of deprivation 2010, Department of Communities and Local Government, England. Douvere, F. (2008). The importance of marine spatial planning in advancing ecosystem-​based sea use management. Marine Policy 32(5): 762–​771. European Commission (2012). Blue growth: Scenarios and drivers for sustainable growth from the oceans, seas and coasts. European Commission, Brussels. Evans, P., Welpton, R. (2009). Business structure database: the inter-​departmental business register (IDBR) for research. Economic and Labour Market Review 3(1):  71–​75. House of Commons, Communities and Local Government Committee. (2007). Coastal Towns, Second Report of Session 2006-​07, House of Commons; London.

 

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Gruber, S., Soci, A. (2010). Agglomeration, agriculture, and the perspective of the periphery. Spatial Economic Analysis 5(1): 43–​72. Hijzen, A., Upward, R., Wright, W. (2010). Job creation, job destruction and the role of small firms: firm-​level evidence for the UK. Oxford Bulletin of Economics and Statistics 72(5): 621–​646. Hynes, S., Farrelly, N. (2012). Defining standard statistical coastal regions for Ireland. Marine Policy 36: 393–​404. Jin, D., Hoagland, P., Wikgren, B. (2013). An empirical analysis of the economic value of ocean space associated with commercial fishing. Marine Policy 42: 74–​84. Kalaydjian, R. (2011). French Marine-​ related Economic data, 2009. Marine Economics Department, IFREMER, Brest, France. Kildow, J. T., McIlgorm, A. (2010). The importance of estimating the contribution of the oceans to national economies. Marine Policy 34: 367–​374. Koehn, Z., Reineman, Kittinge, J. (2013). Progress and promise in spatial human dimensions research for ecosystem-​based ocean planning. Marine Policy 42: 31–​38. Krugman, P. (1991). Increasing returns and economic geography. Journal of Political Economy 99: 483–​499. Morrissey, K., O’Donoghue, C., Hynes, S. (2011). Quantifying the value of multi-​ sectoral marine commercial activity in Ireland. Marine Policy 35(5): 721–​727. Morrissey, K., O’Donoghue, C. (2012). The Irish marine economy and regional development. Marine Policy 36: 358–​364. Morrissey, K., O’Donoghue, C., Farrell, N. (2014). The local impact of the marine sector in Ireland: a spatial microsimulation analysis. Spatial Economic Analysis 9(1):  31–​50. Noble, M. (2004). The English Indices of Deprivation (revised). Office of the Deputy Prime Minister, London. O’Mahony, C., Gault, J., Cummins, V., Kopke, K., O’Suilleabhain, D. (2009). Assessment of recreation activity and its application to integrated management and spatial planning for Cork Harbour, Ireland. Marine Policy 33: 930–​937. Office of National Statistics, Census of Population 2011, Office of National Statistics: London; 2011. Perugini, C., Martino, G. (2008). Income inequality within European regions: Determinants and effects on growth. Review of Income and Wealth 54(3):  373–​406. Pugh, D. (2008). Socio-​economic indicators of marine-​related activities in the UK economy. The Crown Estate, London. Riley R., Robinson C. (2011). Skills and economic performance: The impact of intangible assets on UK productivity growth, UK Commission for Employment and Skills. Riegler, R. (2012). Fragmentation and integration: new evidence on the organisational structure of UK firms. Ph.D. dissertation, University of Nottingham. Rodríguez-​Pose, A., Tselios, V. (2009). Mapping regional personal income distribution in Western Europe: income per capita and inequality. Czech Journal of Economics and Finance 59(1): 41–​70.

 



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Stojanovic, T. A., Farmer, C. (2013). The development of world oceans & coasts and concepts of sustainability. Marine Policy 42: 157–​165. Stojanovic, T. A., Green, D. R., Lymbery, G. (2010). Approaches to knowledge sharing and capacity building: The role of local information systems in marine and coastal management. Ocean & Coastal Management 53(12): 805–​815. Surís-​Regueiro, J. C., Garza-​Gil, M. D., & Varela-​Lafuente, M. M. (2014). Socio-​ economic quantification of fishing in a European urban area: The case of Vigo. Marine Policy 43: 347–​358. The Guardian (2013). Top five UK bankruptcy blackspots all on coast, http://​ www.theguardian.com/​money/​2013/​nov/​25/​bankruptcy-​top-​five-​coast-​blackspots. (Accessed: 17.05.2014) Urquhart, J., & Acott, T. (2013). Constructing ‘The Stade’: Fishers’ and non-​fishers’ identity and place attachment in Hastings, south-​east England. Marine Policy 37:  45–​54. Westling, E., Lerner, D., Sharp, L. (2009). Using secondary data to analyse socio-​ economic impacts of water management actions. Journal of Environmental Management 91: 411–​422. van Putten, I., Cvitanovic, C., & Fulton, E. A. (2016). A changing marine sector in Australian coastal communities: An analysis of inter and intra sectoral industry connections and employment. Ocean & Coastal Management 131: 1–​12. Vega, A., Miller, A., O’Donoghue, C. (2014). Economic impacts of seafood production growth targets in Ireland. Marine Policy 47: 39–​45.

 

 

Chapter Ten

Conclusions

Human activities in the world’s oceans and coasts are at an unprecedented scale and expanding rapidly. There is a maritime dimension to virtually every major issue facing countries around the world today, including the need to adapt to climate change, environmental protection and conservation, international trade, transport and logistics, the security of energy supply, research and innovation and so forth (Schaefer & Barale, 2011). Countries such as China and institutions such as the EU see the ocean, or “Blue Economy” as an integral means of meeting these resourcing needs. However, to date, there has been insufficient coordination of ocean activities and their environmental effects, especially the cumulative effects of multiple activities. A report by the UNEP (2011) estimates that 60 percent of the world’s major marine ecosystems have been degraded or are being used unsustainably. The cumulative economic impact of poor ocean management practices area is estimated at US$200 billion per year (UNEP, 2012). Pressures on the ocean environment include over-​fishing, pollution, habitat destruction, rising water temperatures, declining oxygen levels and acidification and invasive alien species. Marine management as a policy framework has always contained an element of concern about the type and level of economic activity associated with the use of ocean resources. However, to date the information needs of policymakers and managers has focused on data about the marine resource itself rather than the economic environment in which it is used. However, the increasing human pressure on marine resources, the failure to date of single-​sector marine policies to achieve sustainable resource use and the impetus on marine spatial planning (Cicin-​Sain & Knecht, 1998; Douvere, 2008; Ehler & Douvere, 2009) has meant that marine managers now recognize that economic data are indispensable to the management and conservation of the ocean resource. Within this context, an increasing amount of information is being collected on the economic value 159

 

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of goods and services provided by the marine resource. However, much of this information appears scattered throughout a disciplinary academic literature, unpublished government and consultancy-​based reports and across the World Wide Web. In addition, data on the value of the marine economy often appears at incompatible scales of analysis and is classified differently by different authors. In order to make comparative economic analysis possible, a standardized framework for the comprehensive assessment of the marine economy is needed. In response to this challenge, this book presents a conceptual framework for describing and valuing the marine sector at the national, regional and local level. Aiming to engage academics and policymakers, this book conceptualizes the marine sector as a geographically complex, high tech natural resource based sector. Case studies from Ireland and the UK are used to exemplify the range of methodologies available to analysis the economic value of the marine sector across varying spatial scales. Chapters 1 to 3 demonstrated a range of national methodologies, including simple descriptive statistics and input–​output (IO) tables. Chapters 4 to 7 outline regional level methods and outline the regionalization of an IO table and location quotients. Finally, Chapters 8 and 9 describe a series of small area methods and data that may be used to empirically measure the economic impact of the marine resource at the local level. The information created through the application of these methods may be used internationally to better inform future marine planning and investment decisions at the small area level, regional and national level. BIBLIOGRAPHY Cicin-​Sain, B., Knecht, R. (1998). Integrated Coastal and Ocean Management. Island Press, Washington. Douvere, F. (2008). The importance of marine spatial planning in advancing ecosystem-​based sea use management. Marine Policy 32(5): 762–​771 Douvere, F., Ehler, C. (2009). New perspectives on sea use management: initial findings from European experience with marine spatial planning. Journal of Environmental Management 90: 77–​88. Ehler, C., Douvere, F. (2009). Marine Spatial Planning: a step-​by-​step approach toward ecosystem-​based management (IOC Manual and Guides No. 53, ICAM Dossier No. 6. Paris: UNESCO). Schaefer, N., Barale, V. (2011). Maritime spatial planning: opportunities & challenges in the framework of the EU integrated maritime policy. Journal of Coast Conservation 15: 237–​245. UNEP. (2011). Towards a Green Economy: pathways to sustainable development and poverty eradication (a synthesis for policy makers), United Nations Environment Programme, Nairobi. UNEP. (2012). Avoiding Future Famines: Strengthening the Ecological Foundation of Food Security Through Sustainable Food Systems.

 

Index

Andrews, M., 20 Annual Business Survey, 44 Annual Services Inquiry (ASI), 27 backward-​linkage effect, 21, 22 Bijnen, E. J., 113 blue biotechnology, 145 Blue Economy, 2–​3, 43 Border, West and Midlands (BMW), 76 Boschma, R. A., 109, 120 Brand, S., 81 Brett, V., 84 business structure dataset (BSD), 46, 48; data, 146–​47; geographical references within the, 147–​48; index of multiple deprivation for England 2010, 148–​49; marine subsectors within, 147; socio-​economic data,  148 Capone, F., 90, 110 Census of Buildings and Construction (CBC), 27 Census of Industrial Production (CIP), 27 Chang, Y., 95 CILQs. see cross-​industry locution quotients (CILQs)

climate change, 159 cluster development: determinants of, 90; policy focus on, 89 coastal protection, 145 Colgan, C., 6–​8, 12, 19, 48, 147 commercial economic activity, 5 Common Fisheries Policy, 154 core–​periphery structure,  60–​61 Cork Institute of Technology (CIT), 110 Crawley, A., 82 cross-​industry locution quotients (CILQs), 80 Cullinan, J., 129 Doloreux, D., 94 Dutch maritime sector, 95 earnings inequality per county, 140–​41 economic and social changes, 145 economic trend analysis, 44–​45 EMFF. see European Maritime and fisheries fund (EMFF) employment-​based location quotient, 81 English marine sector: 2003 to 2011, 46–​48 environmental protection and conservation, 159

161

 

162 Index

Eriksson, R. H., 109 European Maritime and fisheries fund (EMFF), 154 facilitate linkage, 24 Farrell, N., 129–​31 Farrelly, N., 61 fisheries: and aquaculture, 45; Icelandic fisheries cluster, 20; resources, 55; sea-​fisheries industry,  62; sea-​fisheries sector,  159; sector, 3, 19; single-​species fisheries,  43; socio-​economic impact of, 61 fishing sector, 151–​53 Flegg, A. T., 80, 81 Fløysand, A., 110 forward-​linkage effect,  22 Frencken, 116 GDA. see Greater Dublin Area geography, 60 geo-​referenced information,  133 Ghoshian supply driven (GSD), 25 Ghosh supply-​driven multipliers, 22 globalization, 59 Global Maritime Benchmarking study, 93 Government Office Region (GOR), 146 Greater Dublin Area (GDA), 62 gross value added (GVA), 44 Her Majesties Revenue and Customs (HMRC), 46 Hervas-​Oliver, J. L., 91 holistic value, 2 human activities, 45 Hynes, S., 61, 129 Icelandic fisheries cluster, 20 IMERC. see Irish Maritime and Energy Resource Cluster income spread, 137–​39

input-​output (IO) models, 20, 22–​26, 160 Integrated Maritime Policy (IMP), 153 Interdepartmental Business Register (IDBR), 46 interdependent economy, 21 interregional import–​export statistics, 76 Ireland: North West region of, 84; South East (SE) regions of, 76 Irish Central Statistics Office (CSO), 27 Irish marine economy: data requirements, 26–​27; forward linkages, 30–​32; linkages within the marine sector, 28–​30; production-​inducing effects, 32–​37 Irish Maritime and Energy Resource Cluster (IMERC), 109–​18 Irish maritime cluster: forward linkages, 100–​101; linkages within maritime transportation sector, 98–​100; location quotients, 97–​98 Irish Naval Service (INS), 110 Irish regions, 62–​65; labor market indicators, 67–​69; productivity market indicators, 69–​70; role of marine economy, 65–​67 Ketels, C., 93 Kildow, J. T., 3, 6–​8, 12, 14, 61 Lazzeretti, L., 89, 110 Learmonth, D., 113, 120 Leontief, Wassily, 20 Leontieff structure, 76 Leontief inverse matrix, 24 Leontief supply driven, 24 Leontief supply-​driven multiplier, 21 level data and small area level, 154 linkages, 21–​22 location quotients (LQs) approach, 76 Lower Super Output Area (LSOA), 148, 150

 

Index

macro level Input-​Output (IO), 134 Malmberg, A., 92 marine activity, 2 marine based final demand, 23 marine clusters: IMERC, 109–​18; inter and intra-​linked elements, 109–​11; related variety, 108–​9 marine economy, 2; and clusters, 93–​96; data, 11; data types, 11–​12; definition, 6; international trends, 13; measurement, 6–​10; methodology, 12–​13; multisectoral nature of, 1; multi-​sector industry,  2; at national level, 5; for policy and governance, 55–​56 marine employment, 38 marine employment contribution, 135–​37; county level, 135–​37 marine income contribution, county level, 137 marine industry, 2 marine management, 4 marine renewable energy, 145 marine resources, importance of, 43 marine spatial planning, 46 maritime sector, 2 maritime transport and logistics, 145 maritime transportation sector, 59 Marshallian agglomeration, 62 Marshallian agglomeration theory, 108 Martin R., 82 McIlgorm, A., 7, 12, 14, 61 military, 3 mining, 3 Morrissey, K., 11, 12, 26, 45, 46, 65, 75–​76, 82, 84, 85, 94, 112, 120, 126, 129, 132–​34, 149

163

national-​level analysis,  134–​35 National Ocean Economics Program (NOEP), 8 natural resource-​based products, 61 natural resources management, 126 Neffke, F., 113, 120 New Economic Geography (NEG), 2, 60 NOEP. see National Ocean Economics Program (NOEP) non-​marine final demand, 23 ocean and coastal economies, 7 ocean-​based activity,  4 ocean industry, 2 O’Donoghue, C., 26, 65, 75, 76, 84, 94, 112, 132, 134, 149 oil and gas extraction, 3 Park, K. S., 3, 6, 8 physical–​human interactions,  56 Place of Work Census of Anonymised Records (POWCAR), 133, 134 Porter, M. E., 89–​92, 107, 109, 110 Porters Diamond model, 91–​92 Power, D., 92 Riddington, G., 77 Roe, M., 84 Rossi, D., 20 Round, J. I., 80 Salvador R., 96 sea-​fisheries sector,  59 seaweed production, 45 sectoral monetary transactions, 22 self-​reinforcing,  60 self-​reinforcing and specific regional policy, 95 Shearmur, R., 94 Shinohara, M., 96 shipping and transportation, 3 Sigfusson, T., 19 simple location quotients (SLQ), 79

 

164 Index

simulation model of the Irish local economy (SMILE): calibration, 131–​32; marine, 132–​33; quota sampling (QS), 129–​31 South East (SE) regions, 76 spatial clique, 107 spatial dynamics, 60 spatial microsimulation, 127–​29 spatial microsimulation model, 126 standard industrial classification (SIC) codes for 2007, 146 Sudley, P., 82 Survey of Income and Living Conditions (SILC), 133

Titze, M., 120 Tohmo, T., 81, 85 total regional employment (TRE), 79 University College Cork (UCC), 110 Van Putten, 75 water-​based recreational activities, 45 Westling, E., 148 Wijnolst, N., 93 Williamson, P., 128