Irish Regional Policy Development: Addressing Interstices Through Cluster Analysis

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Irish Regional Policy Development: Addressing Interstices Through
Cluster Analysis
Sean O’Connorα^, Eleanor Doyle* α and Stephen Brosnan α
α Competitiveness Institute, Department of Economics, University College Cork, Western
Road, Cork T12 T656, Ireland.
^ Central Statistics Office, Skehard Road, Cork T12 X00E, Ireland.
*Corresponding author e.doyle@ucc.ie
Abstract
Embracing the systemic complexities associated with regional renewal, current research
emphasises the potential for non-spatially-blind policies to support regional economic
development. In contrast, however, Ireland has instituted regions lacking functional economic
meaning and which, in regional administrative terms, have been deemed ineffective due to
limited resourcing and design. As institutions tasked with distributing structural funds they
have delivered on that purpose. With such a regional policy interstice in Ireland, this paper
provides a quantitative organisation and analysis of Ireland’s economic activity from a regional
cluster-based perspective. The paper proposes these data as a step in developing approaches
at regional levels to support bottom-up economic development, complementing Ireland’s RIS3
approach and appealing to recent progress in Irish regional employment policy. The extent to
which sound foundations for regional policy are derived is explored subject to criteria for
policy effectiveness.
Keywords: Clustering, RIS3, Regions, Policy Interstice, Policy Effectiveness.

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1. Introduction
Interest in clusters and cluster-based policies in Ireland dates back to the Culliton Report (1992)
which highlighted the importance of a competitive business environment to the development
of enterprise and recommended the promotion of clusters focused “on niches of national
competitive advantage” (Doyle and Fanning, 2007:268). Culliton’s policy recommendations
were heavily informed by Porter’s (1990) definition and measurement of clusters, i.e.
“geographic concentrations of interconnected companies and institutions in a particular field”
(Porter, 1998, p.78).
Although clusters have been mentioned sporadically in policy since Culliton, there has been no
evidence of their implementation as part of either a broad (or narrow) set of policies for
economic development or innovation, at national or regional levels. This is in stark contrast to
the popular experience across many industrialized countries (OECD 2007; Borras and Tsagdis,
2008; Njos and Jakobsen, 2016). Much has been gleaned internationally from rolling out
cluster-based policies in ‘place-based’ approaches to regional development (OECD, 2011;
Ebbekink and Lagendijk, 2013) and new research themes of the evolution of clusters (cluster
life cycles) and the role for policy have emerged. A central issue in such debates is that one
policy does not fit all contexts (Todtling and Trippl, 2005), as territories’ systemic and
institutional features play powerful roles in policy outcomes.
Policy approaches emanating from Europe include Constructing Regional Advantage and
Smart Specialisation (Boschma, 2013). Ireland has embraced the latter in its revealed policy
orientation (DJEI, 2014), choosing a predominantly top-down approach to its RIS3 policy
development and designing and implementing a national-level strategy. In related economic
policy development that targets employment growth (across a range of Action Plan for Jobs
1
1
For details on development of these Plans since 2012 see the Department of Jobs, Employment and Innovation:
https://www.djei.ie/en/What-We-Do/Business-Sectoral-Initiatives/Action-Plan-for-Jobs/.

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policy developments and implementation) a more regional emphasis has latterly been
introduced.
In this context, this paper’s first contribution is in providing a quantitative organisation and
analysis of Ireland’s economic activity from a regional cluster-based perspective. Cluster
definitions in Ketels and Protsiv (2014) incorporate benchmark cluster definitions (in Delgado
et al. 2016) into previously developed cluster categories prepared in a first phase of the
European Cluster Observatory Project (ECOP) which had included 38 cluster categories across
302 4-digit NACE activities. The second contribution of the paper is to propose using the
cluster data as first step in developing approaches at regional levels to support bottom-up
economic development. The learnings and understandings from place-based approaches
inform the drawing of implications, for the first time, for regional policy grounded in clusters.
Section 2 outlines contours of modern economic development policy and the role for regional
approaches in the context of Ireland’s Smart Specialisation Strategy for Research and
Innovation (RIS3). A gap in regional policy is identified, offering a context for clusters in an
RIS3 framework. Section 3 first sets out the approach and methods underpinning the analysis
of regional clusters, including data sources and limitations. It then presents comparisons
between economic features of traded and local clusters at different spatial scales across Ireland.
In Section 4 using both concepts and empirical evidence from Sections 2 and 3, foundations
for effective regional policies for Ireland, both justified by evidence and consistent with
national policy are outlined. In considering how effective regional policy might be framed
around clusters, we follow the approach in Lord and Hinks (2010) to consider policy
deliverability and flexibility in addition to ability to be monitored.

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2. Economic Development and Regions
A traditional policy paradigm treats top-down decision making by centralised actors
(government and/or external agencies) as the heart of economic development. Emphasis in
Europe on supports for infrastructural projects and state-sponsored supports for
industrialization via supports to attract subsidiaries of Multinational Corporations (MNCs)
through foreign-direct investment (FDI) characterise this paradigm (Barca et al. 2012).
Underlying perspectives on inputs of capital and efficiency concerns underpin the approach.
Targets for such policies included addressing (regional) unemployment blackspots and
reducing income disparities within and between countries, but were not particularly effective
(Hassink & Klaerding, 2011; Tödtling, 2010).
Embracing the systemic complexities associated with regional issues, research and
international institutions’ attention turned to the potential for non-spatially-blind policies
(OECD, 2009: Barca, 2009; World Bank, 2009). While the role for agglomeration economies
remained important for economic development outcomes, additional place-based features such
as institutions, culture, social characteristics, local policy knowledge and local economic and
policy actors were considered as explanations worthy of research (Vanthillo and Verhetsel,
2012). A switch in emphasis from capital and efficiency considerations towards local
knowledge-based perspectives on growth emerged.
In this spirit, the RIS3 approach highlights knowledge and innovation as drivers of economic
development (Foray et al. 2009) emphasising search processes as part of the entrepreneurial
activities of the economy. Specific ‘winners’ are not picked but the ‘creative destruction’
process is not simply accepted, or monitored, but nudged through appreciation of strengths and
weaknesses of the economic system in terms of its interlinkages, knowledge networks, actors
and institutions. Process elements and historical relations matter for economic evolution, or

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renewal, suggesting that policy development and implementation does not start from a clean
slate.
Initially the Smart Specialisation concept excluded an explicit geographic focus (Boschma,
2013: McCann and Ortega-Argiles, 2011). The rationale for geographic incorporation relates
to how process elements and historical relations suggest a bottom-up requirement for policy
development and implementation. Similarly, entrepreneurial activities and processes through
which future specializations are identified may be identified by targeting developments of local
sectors, or clusters, through engagement with local actors across appropriate knowledge
networks, both private and public, to support the economic renewal process. Given the
structural change that results from renewal, it is recognised that increased and better co-
ordination is called for between regions and national governments and should feature in Smart
Specialisation policies (OECD, 2013).
An additional building block for regional economic development is the different approaches
used by business to develop and innovate (Jensen et al. 2007; Asheim et al. 2007). Three
modes of innovation have been conceptualised as 1) doing, using, interacting (DUI); 2) science,
technology, innovation (STI); and 3) complex, combined innovation (CCI) (Isaksen and
Karlsen, 2012 and 2012b). Traditional policies target the science base and its development
through policies aimed at increasing research investments. In Ireland through the 1990s policies
targeted both building the public research system and infrastructure and individual leading
researchers/investigators with an emphasis on basic research. A more experience-based mode
of innovation, relative to the technical, codification of STI, is characterised as DUI associated
with know-how rather than know-why (Jensen et al. 2007) is acquired on the job, and is similar
to learning-by-using (Rosenberg 1982). The third CCI category represents a knowledge mode
linking both science- and experience-based knowledge from different sources. Incremental

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product changes or new technological platforms are examples of resulting innovations
(Karlsen, 2012).
Conceptual and empirical research on ‘related variety’ considers technology, skills and other
mechanisms through which regional interconnections may be manifest (Frenken et al. 2007:
Neffke et al. 2012). Insofar as clusters are concerned research indicates that linkages that
would otherwise have gone unnoticed based on industry classification have been identified
through the use of cluster categorisations (Best, 2006; Neffke & Henning, 2013). Such
linkages, giving rise to new activities, are the essence of economic renewal which smart
specialisation ultimately targets. As Boschma (2013) points out, the stimulation of
diversification is a primary interest of smart specialisation – in promising domains. Supporting
adaptability and change of clusters are important goals for cluster policy which relate to their
stage in their life-cycle (e.g. Fornhal, 2015) and to the interaction of public and private local
stakeholders.
2.1 Interstices in Irish Policy
The tradition of Irish economic development policy has been one of centralization. In the three
decades from EU (EEC) accession in 1973 the Irish/EU economic development policies of
relevance related to both agriculture and capital developments via the Common Agricultural
Policy (CAP) and Structural Funds. O’Leary (2015) outlines an essentially rent-seeking
approach by Ireland to the CAP that acted to effectively undermine the productive
entrepreneurial capacity in one of Ireland’s largest internationally competitive sectors. With
respect to Structural Funds, European requirements led to regional institutional developments
in the form of Regional Authorities as newly-formed NUTS 3 regions required such institutions
to secure draw-down of funds. This public institutional layer lay between local and national
government.

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With convergence and economic development from the mid-1990s into its Celtic Tiger phase,
five Irish NUTS3 regions no longer qualified for EU Objective 1 status. The institutional
response was to introduce, in 1999, Regional Assemblies with responsibility for two NUTS 2
regions, separating the Objective 1 regions. The meaning of ‘region’ in this context has neither
a functional (economic) meaning, nor in administrative terms have the institutions been deemed
effective, due to limited resourcing and design (O’Leary, 2003).
Penrose (1959) introduced the notion of interstices in explaining that SMEs can target market
segments abandoned by larger firms.
2
Those potentially productive opportunities, despite
revealed rejection by larger enterprises, may be appropriate sources of viable businesses at
smaller scales of production, such that markets are created through the evolution of the
economic system endogenously. Applied to the regional policy context, the interstice concept
relates to potentially productive regional bottom-up approaches that appreciate the place-based
and systemic features underlying economic development. The concept has relevance for
Ireland and the viability of currently absent regional policies in a context of RIS3, where
viability relates to a set of characteristics for policy effectiveness such as, deliverability,
flexibility and ability to be monitored (as outlined in Lord and Hincks, 2010). With respect to
monitoring, a capacity-building outcome of Structural Funds distribution in Ireland resulted in
developed evaluation capacities of policy effectiveness of such spending programmes. These
technical analysis skills, however, appear to have been time-limited as evidenced in comments
by Wright (2010) insofar as the Irish Dept. of Finance was concerned during the Celtic Tiger
period and its collapse.
The interstice in Irish policy development at regional level is potentially costly from the
perspective of path-dependent evolutionary features of regional economies (Benner, 2017),
2
Frigant (2016) applies the concept to the context of the automotive industry.

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evident in relatedness and measurable through regional clusters. Its absence derives, among
other reasons, from the successful centralized strategies used over six decades by the national
inward investment agency (IDA Ireland) to attract some of the most successful MNCs to Irish
sites as platforms into European markets, and beyond. The term ‘foreign assisted firms’ is
locally used to describe the sector, reflecting a state-sponsorship (state-push) connotation
around driving business development.
The deliverability and flexibility aspects of policy effectiveness are explored to in Section 4 to
focus on the potential implications of identifying regional clusters in the context of progress
towards RIS3 objectives.
2.2 A Policy Interstice Made Visible through RIS3
In its RIS3 approach, the Irish government leveraged a National Research Prioritization
Exercise (NRPE) initiated in 2010 with the goal of reviewing achievements in Research and
Development and Innovation (RD&I) over the previous decade to guide future investments in
RD&I. National RIS3 are a required feature - an ex-ante conditionality - for future EU
Structural Fund programmes. So although not designed or planned with an RIS3 agenda in
sight, the NRPE was central to the Irish RIS3 process. The NRPE targets were broad – “to
maximise the impact of public investment on jobs and socio-economic progress” (DJEI, 2015:
6). The NRPE process was collaborative including stakeholders from enterprise, academic and
policy roles.
In identifying priority areas a set of four high-level criteria were used (DJEI, 2014a: 14) i.e.
 The priority area is associated with a large global market or markets in which Irish-
based enterprises already compete or can realistically compete;
 Publicly performed R&D in Ireland is required to exploit the priority area and will
complement private sector research and innovation in Ireland;

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 Ireland has built or is building (objectively measured) strengths in research disciplines
relevant to the priority area;
 The priority area represents an appropriate approach to a recognised national
challenge and/or a global challenge to which Ireland should respond.
The potential role for regional features to drive economic development through RD&I is not
evident (either in NRPE or RIS3). Neither is any appreciation of the nature of alternative
knowledge processes, or path development forces acting on economic development used to
underscore the NRPE process or its outcomes (Appendix 1 outlines the specific Priority Areas
and the Platform Science and Technologies underpinning them identified through the NRPE).
Instead, a structural geographic and old-paradigmatic mind-set of development corresponds to
the Irish RIS3 approach:
“Ireland is small relative to many of its peers in the EU both in terms of its geographical extent
and its economy. Therefore, it was appropriate that the country was treated as a single region
for the prioritisation exercise” (NCC, 2014).
In an RIS3 Peer Review Process
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, feedback and recommendations reflect discussions of
questions and issues raised by Irish policymakers on their RIS3 submission. Of relevance to
regional concerns were identified needs to also target less technologically intensive SMEs, as
well as SMEs in ‘traditional’ sectors. Relatedly, the use of policies to support the development
of clusters featured in the context of SME developments, although clusters were outlined as
needing to include MNCs, researchers and SMEs, and requiring facilitation. Participatory
methods were identified as beneficial to the NRPE outcomes and the subsequent NRPE Action
Group – inclusion in the latter of only state actors and no regional representatives was
highlighted as problematic. An over-focus on public administrators was identified to the
3
Available from the Smart Specialisation Platform at http://s3platform.jrc.ec.europa.eu/regions/IE

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detriment of ‘players’ across the economic system. Regional communication was identified as
a weakness with regional balance needing inclusion.
3 Data for Regional Profiling: Clusters
To generate Irish cluster-based data, recent cluster definitions identified in Delgado et al.
(2016)
4
were used. The definition is based on the various complex linkages across firms and
activities relating to knowledge, skills, technology, inputs and demand, that individually and
collectively generate benefits from co-location of certain types of activities in some locations
relative to others. Co-location patterns are the outcome of that complexity without providing
insight into its specific nature for any individual location or economic activity.
5
A set of 51
distinct clusters was identified.
6
European cluster definitions (Ketels and Protsiv 2014) incorporate definitions identified in
Delgado et al. (2016), into previously developed cluster categories prepared in a first phase of
the European Cluster Observatory Project (ECOP) which had included 38 cluster categories
across 302 4-digit NACE activities. Earlier ECOP work required translation from the US
Standard Industry Classification (SIC) of economic activities to the NACE classification.
Development of the more recent (late 1990s) North American Industry Classification System
provides common industry definitions for Canada, Mexico, and the United States to support
comparable economic analyses.
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4
The method involved grouping 1,088 narrowly defined US sectors (6-digit North American Industry
Classification System, NAICS) in manufacturing and services into a set of Benchmark Cluster Definitions. Data
for 2009 was sourced across County Business Patterns, National Input-Output Tables, and Occupational
Employment Statistics. The method extended earlier classification of traded and local activities using 1996 US
data and 4-digit Standard Industrial Classification (SIC) codes for economic activity and state regions - see Porter,
2003.
5
In a related line of analysis Neffke and Henning (2013) apply the concepts of skill-relatedness and industry space
to identify significant skill linkages across activities that remain unnoticed when traditional activity classification
codes are used. Such research eschews the geographical considerations included in cluster studies.
6
These definitions drive the analyses on the U.S. Cluster Mapping Website http://www.clustermapping.us.
7
Statistical data collected according to NAICS has the added benefit that it can be aggregated into the two-digit
divisions of NACE Rev. 2, facilitating incorporation of the new definitions with relative ease and application to
European data.

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Applying these cluster categories to Irish data, although based on the structure of the US
economy and its underlying inter-linkages, is appropriate for a number of reasons. To date the
available data across European economies is not available at sufficiently granular levels - by
plant - or at geographically appropriate levels to permit assembly of more meaningful cluster-
based data. In addition, the US has been a more fully integrated economy then the European
Union and for a longer period. To the extent that productivity improvements are supported by
such integration the types of benefits of linkages that are encompassed in clusters would be
more likely where market frictions are absent due to integration. In relative terms the
“productivity effect of local externalities” may be expected to be larger for the US than the EU
whereas the extent of the market and Market Access is a “more powerful driver” of productivity
for Europe (Ketels and Protsiv, 2014, p.5). Applying these cluster definitions based on US
data to other economies assume stronger productivity enhancing linkages than may actually be
the case for European economies and regions.
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3.1 Dataset for Regional Clusters
Core data are taken from the Census of Industrial Production (CIP), Annual Services Enquiry
(ASI), Business Demography (BD) from the Irish Central Statistics Office (CSO), and the
International Cluster Competitiveness Project (ICCP) of the Institute for Strategy and
Competitiveness.
9
ICCP data is sourced from the United Nations Commodity Trade Statistics
and International Monetary Fund Balance of Payments Statistics and grouped into cluster
categories. Given the availability of data according to NACE Rev.2, our analysis is conducted
for the period 2008-2012. Data from the CIP is regionally representative for Ireland as it is
generated through annual census. However, the ASI is survey based for all enterprises with 20
persons or more and is considered not regionally representative below NUTS2 level. For
8
Ketels and Protsiv (2014) also make adjustments to European data for activities which are prominent in a
European context, e.g. Ship-Building and Pulp & Paper.
9
Data is compiled by Harvard Business School: https://secure.hbs.edu/login/isc-iccp/index.html?oamreferred=true

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Services enterprises with less than 20 persons engaged, a stratified random sample is selected,
with decreasing sampling proportions taken in the lower ranges. Therefore, data from the ASI
is considered representative at a regional (Nomenclature of Territorial Units for Statistics
(NUTS) 2 level, of which there are two in Ireland).
The CIP data can be organised into 34 separate regions (CSO, 2012d, 2014). Business
Demography data, while having broad industrial and services coverage at an identical
geographic scope to the CIP, is available only for employment data - whereas ASI and CIP
provide data on exports, wages, as well as performance variables including turnover at plant
level. Following Ketels and Protsiv (2014) cluster classifications allow for grouping 406
distinct 4-digit NACE Rev. 2 categories into 51 separate clusters. Of these, economic activity
in 36 clusters can be identified in Ireland.
Data for cluster definitions of Financial Services, Forestry, Insurance Services and Performing
Arts are not provided through either the CIP or ASI. As neither the CIP nor ASI include certain
NACE activities, e.g. NACE A - Agriculture, Forestry and Fishing, some Irish cluster
categories contain fewer 4-digit activities than the definition allows for. For instance,
Agricultural Inputs and Services in Benchmark categories consist of five 4-digit NACE codes,
only one of which is available from the ASI or CIP (specifically, 20.15 Manufacture of
fertilisers and nitrogen compounds). Therefore, employment data from the CIP in this cluster
is understated relative to employment data available from the Business Demography (BD)
source. This is similar for the Education and Knowledge Creation cluster as neither CIP nor
ASI provide information on certain activities. However, in using CIP and ASI data sources,
rather than BD, average wage data is available for each cluster, which is useful when
considering regional living-standards or income disparities, relevant for convergence research,
for example.

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Furthermore, some Irish clusters cannot be reported due to confidentiality issues i.e. when three
or fewer firms dominate the cluster, nationally or regionally. These include; Aerospace
Vehicles and Defence, Appliances, Coal Mining, Electric Power, Jewellery and Precious
Metals, Leather and Related Products, Marketing, Design and Publishing, Music and Sound
Recording, Oil and Gas Production and Transportation, and Tobacco.
The ASI utilises a grossing factor to obtain national and NUTS2 regional-level figures, creating
certain challenges. As some clusters e.g. Hospitality and Tourism consist of NACE data from
across very different 4-digit activities, as well as some clusters requiring a combination of both
ASI and CIP data (e.g. Information Technology and Analytical Instruments), care must be taken
when interpreting figures, as the grossing factors (calculated by the Irish CSO) have not been
constructed with the purpose of amalgamating data into BCD.
3.2 Regional Cluster Trends
Drilling into the regional picture, Irish employment data were taken from the BD, with wages
and output (Gross Value Added) drawn from CIP and ASI sources. Given NUTS2 limitations
of our ASI data, output is limited to two NUTS2 regions in Table 1.
[insert Table 1 around here]
Shares of employment are most concentrated in local activities in both regions. The South and
East region exhibited higher employment concentration in traded activities. In both regions,
over 75% of GVA is contributed by traded activities and the share, at 87%, is highest in the
Border, Midlands, West region. Export shares are over 97% for traded activities in both
regions.
In terms of prosperity indicators for the regions, average wages are higher for both traded and
local activities in the South and East region: the average traded wage was 28% higher and local
wage was 12% higher. Within the South and East region, workers in traded activities earned

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62% more than workers in local activities: the comparable figure for the BMW region was
43%. Annual wage growth in traded activities of 1.8% was faster in the BMW region than
1.1% observed for the South and East region. In both regions local average annual wages
declined - decline was faster for the South and East (-6.4%) than in the BMW region (-3.4%).
Since data on Services are not considered representative at more granular geographic levels,
further consideration of local and traded clusters focuses on employment data as outlined in
Table 2. Table 2 provides a breakdown of traded and local employment shares across Ireland’s
8 NUTS3 regions.
[insert Table 2 around here]
We note similarity to national and NUTS2 analysis with an average 60%/30% split between
traded and local employment. One outlier is the Midlands region
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(13.8% traded employment
share) which also saw the largest decrease in traded employment during the period. While
traded activities pay higher average wages, and generate higher levels of GVA and exports,
local activities, were more susceptible to the contractionary employment effect of the economic
downturn from 2008-2012 (echoing Tables 1 and 2).
3.3 Exploring Traded Clusters
Table 3 provides data for selected clusters on employment, wages and their respective growth
rates from 2008-2012 (only those clusters achieving a top 10 ranking for employment share,
wage level or wage growth - in bold – are included). Average cluster wages in 2012 ranged
from €60,915 and €60,422 in Upstream Chemical Products and Biopharmaceuticals
respectively, to a low of €19,510 for Hospitality and Tourism (second highest employment
share).
10
The reason for the Midland’s high share of Local employment is due to presence of the Department of Education
and Skills in the region. This means the majority of educational employment is noted as being based here.

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[insert Table 3 around here]
Only 7 clusters recorded positive employment growth rates between 2008 and 2012. Of these,
Medical Devices and Livestock Processing are in the top 10 for employment shares (2012).
Textile Manufacturing, ranked 22 out of 36 in 2012 for average wage level, exhibited the fastest
wage growth (6.2% per annum) over the period, followed by Information Technology and
Analytical Instruments (5.2%). While Video Production and Distribution exhibited the fastest
growth rate in employment, it experienced the greatest annual wage decline, at 4.1%.
Given Ireland’s reliance on exports to fuel economic growth (Brady et al., 2013), Table 4
presents the top ten best performing clusters by export value. Although data on GVA, wages
and exports is not available from the CSO for Insurance Service or Financial Service, export
data are available from the Institute for Strategy and Competitiveness for Ireland (Porter and
Bryden, 2015) through its International Cluster Comparison Project (ICCP).
[insert Table 4 around here]
The combined value of exports from these ten clusters accounted for 65% of Irish exports. Nine
of the clusters also appear in the highest ranking clusters by employment (Table 3).
Downstream Chemical Production ranked 17th.
Biopharmaceuticals, Information Technology and Analytical Instruments, along with Business
Services and Food Processing are some of the Irish traded clusters which generated the highest
export values. Information Technology and Analytical Instruments, Transportation and
Logistics, Production Technology and Heavy Machinery and Downstream Chemical
Production all witnessed declines in export values over the period.
The Business Services cluster witnessed a notable substantial increase in exports between 2008
and 2012 of over 55%. By 2012 exports of Irish Services had almost caught up with

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merchandise exports – by 2014 services exports edged ahead of merchandise exports for the
first time. In the time frame between 2000 and 2014 the share of Irish Services exports
increased from 22% to over 50%, relative to international shares of 20.7% and 21.2%
respectively (see Byrne and O’Brien 2015). The main sectors accounting for this shift were
Business Services which grew from 18% in 2000 to 29% by 2014 (aggregate Irish services
exports) and Computer and Information Services which grew from 35% in 2000 to 42% in
2014. A decline in merchandise exports of Office Machinery (declining from 35% in 2000 to
7% in 2014) reveals the extent of the structural shift in the economy and reorientation towards
services. This points to the relevance of using cluster definitions in analysing economic trends
since the increasing importance of services demands greater attention and analysis both in
terms of its export contribution and also given its linkages across other activities.
The substantial difference in exports per plant between foreign and Irish plants in Table 4 while
notable is not surprising given the prominence of foreign firms for Irish export performance,
along with the impact “contract manufacturing” can have on official statistics (O’Leary, 2015).
The duality of the Irish economy in terms of Irish and foreign-owned economic activity has
been widely studied (e.g. Barry and Hannan, 1995; Barry and Bradley, 1997; Barry et al. 2005;
O’Leary, 2015; Breathnach et al. 2015). In this context we disaggregate the 5 largest clusters,
by exports (2012 value), into Irish and foreign-owned plants in Table 5 to provide further
information on their relative employment, output and wages per worker trends.
[insert Table 5 around here]
Similar to Table 4, foreign plants display higher employment intensity, and also generate higher
GVA per plant and wages. In the Food Processing cluster comparing the relative productivity
of Irish to foreign plants indicates that output per foreign plant is 50 times greater than their
Irish counterpart (the largest productivity differential across the 5 clusters), while wages per

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plant are 1.7 times greater. While these clusters play a key role in reported Irish economic
activity, much of the productivity is generated from foreign rather than Irish plants. The
average employment differential across the selected clusters is over 5.5 i.e. foreign plants
employ over five times as many employees; for output the average differential is 26. However,
for wages it is 1.5 indicating that labour market forces rather than relativities in output or
productivity drive discrepancies in wage rates.
3.4 Regional Cluster Concentrations
Utilising Haig’s (1926) Location Quotient (LQ) we identify at the NUTS3 level, those regions
where specific traded clusters have strong geographic employment concentrations. The LQ
method has been utilised previously to determine the location of a cluster, although with a non-
Porterian connotation (Miller et al. 2001; Kelton et al. 2008).
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We utilise the LQ method to
map 14 clusters, selected due their export performance from 2008-2012 - the ten reported in
Table 5 along with Agricultural Products and Inputs, Financial Services, Insurance Services
and Transport and Logistics, in which Ireland displays export advantages. Table 7 presents the
LQ scores for 2012 for these clusters.
[insert Table 7 around here]
An LQ score greater than 1.0 (in bold) indicates employment in a specific cluster is more
concentrated in the region relative to the national share. Values underlined indicate the highest
regional LQ score for each cluster. The highest cluster concentration for each region is
presented within a box.
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The LQ is not without its drawbacks (Woodward & Guimarães, 2009) as, for example, it does not take into
account the number of plants within a region and may be hampered by predetermined definition of appropriate
spatial units. Research on Germany (Scholl & Brenner, 2014) and Ireland (van Egeraat et al. 2015) address these
issues using geo-coded plant data but at 2-digit and 3-digit industrial classifications.

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For the Border region concentration is evident in Agricultural Inputs and Services, (at 2.5),
however, this cluster is most highly concentrated in the Mid-West (3.0). The Border exhibits
the highest regional LQ score for one cluster i.e. Food Processing (2.2).
Of the six clusters in which Dublin exhibits concentrations, four of these are regional LQ
highest scores i.e. Financial Services (2.0); Insurance Services (1.9); Communications
Equipment and Services (1,8); and Business Services (1.4). The Mideast exhibits regional
high concentrations in Livestock Processing (3.6) and Information Technology and Analytical
Instruments (2.8) clusters. The Midlands’ only concentration in Livestock Processing (1.6) puts
it in the top four locations for that cluster, across eight regions.
For the Mid-west, two clusters occupy the highest regional cluster measures in both
Agricultural Inputs and Services, and Downstream Chemical Products (both at 3.0). In the
South-east, the most clustered activity is measured in Livestock Processing (3.5) and
Agricultural Inputs and Services (2.7) but the cluster with the leading regional value is
Biopharmaceuticals (2.2). In the South-west, the highest concentration is evident in
Production Technology and Heavy Machinery (2.3) for which the region also has the highest
regional LQ score. For the West region, of its three observed clusters, one has the highest
regional LQ score i.e. Medical Devices (4.0). The Transportation and Logistics cluster,
unsurprisingly, has the least dispersion across the regions in its employment concentration with
the highest LQ scores in Dublin, Mid-West and South-East, respectively.
A regional wage comparison is only possible at NUTS2 level. A marked difference is evident
in wages between specific clusters in the South & East (SE) region relative to the Border
Midland, and West (BMW) region. A differential of €20,000 is evident for wages in
Biopharmaceutical and Information Technology and Analytical Instruments between the two
regions.

19
The final element in our analysis focuses on the important issue of relating traded clusters to
regional economic outcomes. In the context of Table 7 we reviewed wage-growth trends by
cluster, from Table 3. Concentration by specific regions in specific clusters and the relative
wage rates of different clusters feed into regional average wages. For example, regions with
employment concentration in Livestock Processing experienced not only declining wages, but
as wage levels in that sector are the fifth lowest of all clusters, the impact of such clustering is
compounded in poor regional performance.
Across our NUTS3 regions we computed the change in regional average wages (2008-2012)
and apportioned the change into two effects; a wage level effect and a cluster mix effect
(following Porter, 2003).
12
The level effect is found by the sum of differences between a
region’s wage in each cluster and the national average for that cluster times the region’s
employment share in that cluster. The mix effect is found by the sum of differences in each
cluster’s employment share compared to the national proportion times the cluster’s national
average wage.
We observed a duality in the results with Dublin, Mid-East, Mid-West, and South-west regions
displaying positive mix and level effects. These regions compete disproportionately positively
in employment terms in clusters with relatively high wages. They also outperform in average
wage terms i.e. relative to the national average wages in those clusters in which they compete.
The four other regions exhibit both negative mix and level effects. The level effect represented
39% on average of the variation in average wages across regions and 61% for the cluster mix
effect.
13
In identifying implications of our analyses, we return to these effects.
12
Non-availability of services data at NUTS 3 level limits this analysis to CIP data only.
13
These are in contrast to the shares found for the US (including services data) of 76% and 24% (Porter, 2003).

20
4 Discussion and Implications
In drawing the above conceptual and empirical evidence together, this section identifies
foundations for effective regional policies for Ireland, both justified by evidence and consistent
with national policy, as outlined in Lord and Hinks (2010). Both policy deliverability and
flexibility are required for effectiveness, in addition to ability to be monitored.
A cluster-based categorisation of Irish economic activity reveals regional concentrations across
a set of activities in which Irish-based business, indigenous and FDI, is internationally
competitive. While employment patterns can be analysed from the data, limited availability of
wages data, mean it is challenging to identify regional cluster-based policies. While revealed
presence of agglomerations of clustered activities indicate the extent of concentrations of
economic activity, the addition of wages data allow for linking business activity to cohesion-
related measures. The evidence base on which to relate clustering to regional living standards
requires data on wages: policy deliverability and appropriate monitoring demand such data.
Further data enhancements are evident internationally in matched employer-employee data
permitting, e.g., estimating the amounts of human capital each employee supplies to their
employer measured as a skill-mix based on both experience and education, and how this
changes over time (Hijzen 2006; Kersley et al. 2006). Standard measures of workforce quality
are years of education and experience, while standard measures of firm characteristics include
industry and firm size. Such measures are considered to “fall woefully short” (Abowd,
Haltiwanger and Lane, 2002:5) for research focusing on how the heterogeneity of workers and
firms adds to understanding the link between human capital and productivity. Such data would
offer further foundations on which to base policy, but have not been widely adopted, and are
administratively burdensome and costly to develop. Matching the need with developing
regional intelligence for RIS3 for Irish regions could increase the likelihood of such research
being conducted.

21
Nonetheless, the data above underlines the important role played by internationally competitive
clusters and relative wage rates for regional (and national) prosperity and convergence, and
cohesion goals. We find that half of the NUTS2 regions do not enjoy the appropriate business
conditions to support higher wage rates achievable in the same clusters in other Irish regions.
Within specific clusters, regional differences are evident with potential to benefit from
alignment of policy objectives and local experience. While it represents only one aspect of
explaining the outcome, a tendency to orient R&D supporting activities to a science-push
agenda ignores potentially useful supports for more user-driven knowledge development and
on approaches where both knowledge modes are balanced. This aligns with proposals in
Breathnach et al. (2015: 514) to employ “sectoral selectivity in the attraction of inward
investment” for regions, with the additional requirement of enhancing it with understandings
of regional development needs, beyond research and innovation (as in RD&I). Jensen et al.
(2007) point out that European data produced, e.g., in its ‘innovation scoreboard’ suffer from
over-reporting on S&T indicators (R&D spending; patenting, etc.) relative to measures for
capturing organisational aspects of doing, using, interacting. Such measures may focus on
“core high performance work practices”, the “extent to which functions are integrated” and
extent of customer relations (Jensen et al. 2007: 686). Identifying the more specific innovation
needs of cluster firms in different regions offers opportunities to differentiate supports through
different elements of integrated regional cluster programmes, integration making sense where
regions can cooperate.
In addition, the same regions exhibit lower wages due to the range of clusters in which they
regions concentrate – and this is the larger factor in explaining differences between average
regional wages and the national average. This is of particular concern given path-dependency
arguments where regions can experience lock-in to potentially low-trajectory growth
associated with self-reinforcement of existing economic concentrations. While Ireland’s RIS3

22
is silent on the specifics for addressing regional convergence, despite its broad goals of growing
jobs and socio-economic progress, the experience in terms of policy development and
implementation in relation to employment and skills offers some promise.
We have argued that an interstice of substantive regional policy characterises decentralized
government layers in Ireland. Regional structures designed and redesigned ostensibly to
administer externally-secured regional aid need repurposing. New structures and
configurations of institutions are emerging through the roll-out of the national Action Plan for
Jobs (a programme of the Department of Jobs, Enterprise and Innovation) pointing to
appreciation of not only regional roles but responsibilities. Over 2015 and 2016 eight regional
plans have been developed targeting employment and achievement of ‘regional potential’.
New networks have been instigated consisting of local actors across the enterprise sector, local
authorities, enterprise agencies, and other public bodies, tasked with implementation and
monitoring. Regional Skills Fora (organised through a different government i.e. Department
of Education and Skills) target increased engagement between education and training systems
locally and employers and other enterprise stakeholders. In a preliminary review of the Action
Plan, the OECD (2014: 27) considered the approach as marking “an important innovation in
Irish governance” and identified potentials for enhancing horizontal and vertical governance
mechanisms. Much of the language and approach across the review points to smart-
specialisation bottom-up processes to initiate strategic initiatives, including through making
“better use of information and data at the sub-national level” (OECD, 2014: 30).
Regional profiling as a feature of RIS3 would necessitate analyses of innovation experiences
and modes by firms. The broader innovation experience across firms in Ireland indicates that
sectoral type does not explain the likelihood of innovation and similarly firm size has no
bearing (Doran and O’Leary 2011). An ownership disadvantage has been identified such that
indigenous firms are less likely to engage in product innovation and yet where indigenous firms

23
engage in intramural R&D, they are more likely to introduce new products (Doran et al, 2013).
Indigenous firms display lower likelihoods of interacting with external actors or agents for
innovation than foreign-owned businesses. However, indigenous firms’ innovation outputs are
driven by such external interactions, in addition to their expenditures on R&D (Doran and
O’Leary 2014). Implications for regional clusters and policy point again to further data
collection efforts to best target supports, and to design differentiated interventions appropriate
to firms’ needs in regional contexts.
The requirement for such interventions is increasingly necessary since despite increased
expenditures and development of the STI infrastructure in Ireland across the 1990s and 2000s,
the percentage of firms engaging in innovation (product or process) has not increased (Roper
and Dundas 2011). Regions with weaknesses in terms of their innovation systems, insofar as
STI-based firms are concerned, may need to focus on the CCI and DUI innovation modes but
these too demand experienced and skilled labour and availability of non-R&D based business
services (Isaksen and Karlsen 2012). Links to employment focussed regional Action Plans,
thus, offer means to target new Development-focused directions in enterprise supports.
More specifically, the extent to which a regional cluster concentration of economic activity is
characterized by different innovation modes appears as a necessary element in advancing
regional economic strategies. The relative importance of the modes to different cluster
members (firms) may allow for appreciation of alternative policy supports and local
stakeholders. Interrogation of current local regional and cross-regional networks across the
modes may support identification of where bridge-building may be appropriate, cross-
regionally as well as across clusters. The potential mindset of denigrating DUI-type linkages,
however, may need to be overcome given the conclusion in one cluster study that “co-operation
in technical problem solving, although helpful, is not likely to have a significant impact on
innovation” (O’Connell et al. 1997).

24
Regional cluster-based policy appears matched to the flexibility necessary for place-based
business’ needs and their knowledge-mode supports. Deliverability is feasible via regional
fora, already targeted with serving local employment and skills development objectives. In
terms of the third important feature, ability to be monitored, cluster-related measures as
outlined here (employment, wages, and absolute size (in employee or enterprise numbers)
which affect the number and intensity of potential linkages) feed into understanding regional
profiles and any policy impacts (Ketels and Protsiv, 2014). Across regional Action Plans, a
disciplined focus is maintained for a range of objectives with targeted metrics identified as
specific responsibilities for different state funding agencies in science, health, education
agriculture, marine, and environment domains, as well as additional targets for government
departments and the business development agencies supporting indigenous and FDI sectors.
Targets exhibited in each regional Action Plan (in addition to the national Plan) require
monitoring with progress published at six-monthly intervals, including activities not
undertaken (and why) and revisions of activities based on improved understandings of
Regional Fora.
By combining RIS3 and clustering approaches, it would appear that opportunities now exist
for closing place-based policy interstices and developing and delivering regional economic
development policies.

25
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29
Appendix
Table A1: Priorities and Platform Science and Technologies from National Research
Prioritisation Exercise
NRPE Priorities
Future Networks and Communications
Data Analytics, Management, Security and Privacy
Connected Health and Independent Living
Medical Devices
Diagnostics
Therapeutics – Synthesis, Formulation, Processing
and Drug Delivery
Food for Health
Sustainable Food Production and Processing
Marine Renewable Energy
Smart Grids and Smart Cities
Manufacturing Competitiveness
Processing Technologies and Novel Materials
Innovation in Services and Business Processes
Platforms (Science and Technologies)
Basic Biomedical Science
Nanotechnology
Advanced Material
Photonics
Software Engineering
Source: NCC (2014).

30
Table 1: NUTS2 Traded and Local Economic Activities 2008-2012
Variable
Traded Activities
Local Activities
South &
East
Border,
Midland, West
South &
East
Border,
Midland, West
Share of employment (%) –
2012
37.2
28.0
62.8
72.0
Employment growth 2008 -
2012 (CAGR) (%)
-3.3
-5.1
-4.8
-5.2
Share of output GVA (%) -
2012
76.6
87.1
23.4
12.9
Share of exports (%) - 2012
97.4
97.7
2.6
2.3
Average Wage (2014€)
42,878
33,620
26,379
23,568
Wage growth 2008-2012
(CAGR) (%)
1.1
1.8
-6.4
-3.4
Sources: Employment data is taken from Central Statistics Office (2008b & 2012b).
Output and Wage data is taken from Central Statistics Office (2008a, 2008c & 2008a, 2012c).
Table 2: NUTS3 Traded and Local Activities 2008-2012
Region
Share of employment (%) 2012
Employment growth 2008 -2012
(CAGR) (%)
Traded
Activities
Local
Activities
Traded Activities
Local Activities
Border ^
36.6
63.4
-5.5
-8.7
Dublin
35.0
65.0
-3.2
-4.1
Mid-East
39.0
61.0
-5.9
-8.0
Midlands ^
13.8
86.2
-10.2
-4.0
Mid-West
38.7
61.3
-4.6
-8.7
South-East
38.0
62.0
-5.5
-9.6
South-West
29.4
70.6
-12.3
-7.3
West ^
32.8
67.2
-5.6
-7.5
Sources: Central Statistics Office (2008b & 2012b) – Business Demography.
Note: ^ These regions retain Objective 1 Status, as defined by EU.

31
Table 3: Selected Traded Clusters in Ireland, 2008-2012.
Cluster
Employment,20
12
CAGR
Employment
2008-2012
Empl. Growth
Rank
[1=highest
growth]
Average
Wage 2012
(2014€)
Wage Rank
[1=highest
growth]
CAGR
average wage
2008-2012
Wage Growth
Rank
[1=highest
growth]
1
Business Services
119,004
-3.6
19
42,681
13
3.5
6
3
Distribution and Electronic Commerce
80,642
-3.4
16
43,419
11
4.7
5
4
Transportation and Logistics
39,159
-3.2
15
48,432
8
1.4
18
5
Information Technology and Analytical
Instruments
26,265
-8.5
24
59,180
3
5.2
2
6
Medical Devices
24,109
4.9
3
41,537
14
3.0
9
7
Food Processing and Manufacturing
21,501
-2.2
11
45,554
9
1.0
21
8
Biopharmaceuticals
14,621
-0.5
8
60,422
2
3.0
8
9
Livestock Processing
13,406
1.7
5
28,243
32
-1.2
29
14
Communications Equipment and
Services
5,659
-3.5
17
57,559
4
5.2
3
17
Downstream Chemical Products
4,351
-9.9
26
50,942
7
1.9
13
18
Education and Knowledge Creation
4,277
1.0
6
51,830
6
-0.6
25
20
Upstream Chemical Products
3,239
-1.8
9
60,915
1
0.8
22
21
Video Production and Distribution
3,111
8.7
1
39,433
16
-4.1
36
25
Wood Products
2,753
-18.8
34
33,953
25
2.9
10
26
Recreational and Small Electric Goods
2,336
6.9
2
33,955
24
1.6
16
28
Water Transport
2,043
-3.6
18
55,349
5
3.4
7
29
Textile Manufacturing
1,862
-11.7
29
36,225
22
6.2
1
31
Fishing and Fishing Products
1,830
0.12
7
25,378
34
0.5
23
32
Environmental Services
1,788
3.12
4
32,762
29
-2.4
34
34
Apparel
1,209
-18.0
33
22,994
10
-2.2
33
35
Agricultural Inputs and Services
368
-2.0
10
43,901
35
1.6
15
36
Furniture
103
-2.5
12
27,674
31
4.7
4
Sources: Central Statistics Office (2008a, 2008c, 2012a, 2012c) –Annual Services Enquiry and Census of Industrial Production.

32
Table 4: Top Ranked Traded Clusters by Exports: Ireland 2012
Rank
Cluster
Export Value
(€,000)
CAGR
2008-
2012
Per
Irish
Plant
(€,000)
Per
Foreign*
Plant
(€,000)
1
Biopharmaceuticals
31,818,151
4.1
5,909
704,838
2
Information Technology and
Analytical Instruments
23,354,219
-6.0
1,987
176,820
3
Business Services
20,141,212
55.4
335
28,197
4
Distribution and Electronic
Commerce
15,272,480
21.8
444
16,254
5
Food Processing
14,440,756
3.4
9,334
464,229
6
Medical Devices
8,359,329
13.5
7
Livestock Processing
2,573,482
4.5
8
Transportation and Logistics
1,612,012
-10.6
9
Production Technology and
Heavy Machinery
1,333,948
-3.7
10
Downstream Chemical
Production
748,975
-2.7
TOTAL
119,654,564
Total Merchandise Exports
94,154,125
1.4
Total Service Exports
90,920,187
7.5
Notes:
*Foreign is defined as non- indigenous Irish business.
Sources: Central Statistics Office (2008a, 2008c, 2012a, 2012c, 2016a, 2016b) – Annual
Services Enquiry, Census of Industrial Production and Balance of Payments.
Table 5: Traded Clusters: Irish and Foreign Plants 2012
Employment
per plant
Output per plant
(€,000)
Wages per worker
per plant
Rank
Cluster
Irish
Foreign
Irish
Foreign
Irish
Foreign
1
Biopharmaceuticals
62
295
318,884
671,294
46,081
61,560
2
Info. Tech. &
Analyt. Instruments
14
113
1,238
59,742
46,990
62,336
3
Business Services
8
57
354
6,335
38,395
50,904
4
Distribution &
Elec. Commerce
10
27
613
7,036
33,717
65,827
5
Food Processing
51
260
4,469
223,134
36,877
62,197
Sources: Central Statistics Office (2008a, 2008c, 2012a, 2012c) – Annual Services Enquiry
and Census of Industrial Production.

33
Table 6: Location Quotients: 14 prominent Irish traded clusters: NUTS 3 Regions, 2012.
Cluster
Border1
Dublin
Mid-East
Midland1
Mid-West
South-East
South-West
West1
Agri. Inputs & Serv.
2.5
0.2
1.5
0.5
3.0
2.7
1.3
0.5
Biopharmaceuticals~*
0.5
0.8
1.2
0.0
0.0
2.2
2.0
1.4
Business Services^*
0.5
1.4
0.8
0.2
0.9
0.6
1.1
0.5
Comms. Equipment &
Services~
0.1
1.8
0.3
0.1
0.4
0.4
0.7
0.2
Distribution & Electr.
Commerce^*
1.0
1.1
0.9
0.4
1.2
0.8
1.1
0.7
Downstream Chemical
Products
0.8
0.6
2.4
0.2
3.0
1.3
1.5
0.2
Financial Services
0.2
2.0
0.3
0.0
0.1
0.1
0.4
0.2
Food Processing*
2.2
0.6
1.3
0.4
0.9
2.1
1.8
0.3
Information Tech. &
Analyt. Instruments^*
0.3
0.7
2.8
0.1
2.0
0.5
1.7
0.9
Insurance Services
0.7
1.9
0.2
0.1
0.2
0.7
0.2
0.2
Livestock Processing
1.6
0.1
3.6
1.6
1.0
3.5
0.6
0.9
Medical Devices^
2.1
0.1
0.3
0.9
2.0
1.7
1.1
4.0
Production Tech. &
Heavy Machinery
1.5
0.2
0.9
1.0
1.2
1.9
2.3
1.7
Transportation and
Logistics^*
0.9
1.3
0.8
0.3
1.2
1.1
0.6
0.5
Notes:
LQ scores in bold indicate regional concentration (>1.0) relative to national employment share.
LQ scores underlined indicate highest cluster concentration relative to national employment shares.
LQ scores within a box indicate the most concentrated cluster in that region.
1 denotes these NUTS3 regions make up the NUTS2 region BMW. The reminder constituent the South-
East
^ denotes clusters in the top 5 by employment share (see Table 4).
~ denotes clusters in the top 5 by wage level (see Table 4).
* denotes clusters in the top 5 by export share (see Table 5).
Sources: Central Statistics Office (2012b) – Business Demography.