Why data matters for shipbuilding industrial policy
Shipbuilding underpins competitiveness, security, and the energy transition, yet policy decisions are often taken with incomplete evidence. Fragmented and inconsistent data reduces visibility on how the sector is performing and limits the ability to design and assess effective industrial interventions.
Insufficient evidence reduces the precision of productivity-related policy interventions. Limited insight into where value is created, and which factors drive performance hinders effective allocation of support.
Workforce pressures are harder to anticipate. Gaps in skills and labour market information delay policy responses in a sector that depends on specialised capabilities and long training pipelines, increasing costs and risks to delivery.
Progress on innovation and decarbonisation is difficult to quantify. Data on these developments are not collected systematically or consistently across countries or firms, obscuring whether new technologies and low-emissions solutions are scaling.
Improving shipbuilding data is a strategic enabler, not a technical exercise. Stronger frameworks, clearer definitions, and international co-ordination, building on OECD work, can strengthen policy targeting, evaluation, and international benchmarking.
Shipbuilding is strategically important — but statistically invisible. Across major shipbuilding economies, governments are making decisions on industrial support, skills, and the energy transition with partial, inconsistent, or poorly aligned data on the sector. This limits the effectiveness of industrial policy, complicates international comparability, and constrains the ability to evaluate outcomes.
Recent analysis across 11 countries and the European Union highlights four key consequences of these data limitations:
1. National definitions of the shipbuilding industry differ across countries and fail to reflect its full complexity, restricting the analytical consistency of international benchmarking and reducing opportunities for mutual learning.
2. Productivity drivers cannot be clearly identified or leveraged, limiting the ability to target support where industrial returns are highest.
3. Skills shortages are detected late, increasing adjustment costs and reducing the effectiveness of labour market and training policies.
4. Progress in technological upgrading, innovation and decarbonisation cannot be reliably tracked, weakening alignment between industrial and sustainability objectives.
While these challenges are largely illustrated analysing the United Kingdom in the Data Collection Toolkit report (OECD, 2025[1]), they are widely shared internationally. Addressing them requires co-ordinated action to improve definitions, data capabilities, and knowledge exchange, enabling shipbuilding to be effectively integrated into national industrial strategies.
Industrial policy depends on clear evidence of where value is created, where constraints emerge, and which interventions deliver results. Shipbuilding is a complex, assembly-intensive industry encompassing activities from design and engineering through to final outfitting. A substantial share of value creation takes place outside the shipyard, as intermediate inputs supplied by ancillary industries, such as marine equipment, steel and coatings, account for around 70–80% of the value of final output (Gourdon and Steidl, 2019[2]). In shipbuilding, however, this structural complexity is compounded by a fragmented data landscape, limiting visibility across firms, regions, and value-chain activities. Information on productivity, capital intensity, workforce composition, and technological upgrading is often partial or difficult to compare, both within and across countries.
Policy decisions are frequently informed by fragmented indicators, informal intelligence, or firm-level case studies rather than by systematic, comparable evidence. This constrains the ability of governments to allocate resources efficiently, to anticipate structural risks, and to benchmark performance internationally. More fundamentally, shipbuilding does not consistently appear as a coherent analytical object within official statistics, underscoring the need for policy-relevant data frameworks that organise existing information in ways that reflect how the sector functions as an industrial system.
Varying industry definitions
Shipbuilding spans multiple activities and stages of the value chain, including new construction, repair and retrofitting, marine equipment, offshore structures and defence-related activities. In practice, however, existing industry definitions do not always reflect this breadth and vary significantly across countries, limiting international comparability and analytical consistency. Some classification systems define shipbuilding narrowly, focusing on the physical construction of vessels, while others adopt a broader view of shipyards as integrated industrial hubs that also undertake repair, conversion and specialised services.
These differences are evident across major statistical frameworks. International standards typically group civilian and military ship construction together, whereas European classifications distinguish defence-related shipbuilding from civilian production (United Nations Statistics Division, 2023[3]); (Eurostat, 2025[4]). In North America, definitions are broader still, encompassing most activities linked to shipyards, including ship repair and dismantling, niche segments such as yacht building, and emerging activities such as unmanned and robotic vessels as well as underwater remotely operated vehicle manufacturing (United States Census, 2026[5]). Japan, by contrast, integrates ship repair and marine engine production within shipbuilding, while Korea further disaggregates shipbuilding activity by construction characteristics, such as by primary materials (e.g. steel or synthetic resin ships) (Ministry of Internal Affairs and Communications, 2023[6]; Kostat, 2024[7]).
Such variations reflect national industrial structures and policy priorities rather than a shared understanding of what constitutes shipbuilding. As a result, headline indicators, such as employment, output or productivity, may capture different underlying activities across countries and should not be compared at face value. This underscores the need for careful interpretation of international data and, where possible, greater transparency and harmonisation in the definition and reporting of shipbuilding activities.
Productivity drivers cannot be identified or leveraged
Productivity is central to industrial competitiveness, yet limitations in domestic shipbuilding data makes it difficult for policymakers to identify where productivity is highest and why. Without consistent and granular information on value added, employment, capital intensity, and output, it is difficult to determine which shipyards, regions, or segments generate the greatest productivity gains and which ones are lagging. While productivity is usually mentioned by countries in industry assessments or targets for the shipbuilding industry, few statistical offices report specific indicators to measure it. Where productivity is measured, indicators typically apply to the broader maritime sector, and such indicators vary across countries limiting their comparability.
Observed differences may reflect scale effects, technology adoption, workforce skills, or integration into global value chains, but existing data rarely allows these factors to be disentangled. This lack of clarity weakens industrial policy. Governments struggle to distinguish between firms that require support to upgrade capabilities and those that are already operating at the technological frontier with potential to scale. It also complicates the evaluation of policy effectiveness, as changes in productivity cannot be robustly linked to specific interventions. In this context, productivity-enhancing policies become less targeted, less effective, and harder to justify, particularly where public resources are constrained.
Skills shortages are detected too late
Labour and skills indicators are among the least consistently available in shipbuilding. A key challenge is the absence of clear and standardised definitions, particularly when distinguishing between direct employment in shipyards and indirect employment across subcontractors and suppliers. This is compounded by the sector’s heavy reliance on subcontracting and project-based work, which makes employment levels difficult to estimate using conventional statistical instruments. Limited public data on workforce composition, occupational structures, and subcontracting further constrains the accuracy and completeness of employment estimates. Information on employment, vacancies, apprenticeships, and wages is often incomplete, or insufficiently granular to capture local or occupation-specific bottlenecks. Evidence on skills demand is therefore frequently derived from ad hoc sector studies or international classification frameworks, such as the U.S.’s Occupational Information Network (O*NET) and the EU’s European Skills, Competences, and Occupations (ESCO) classification, rather than from systematic and comparable national statistics. When these gaps persist, emerging skills shortages tend to become visible only once they are already constraining production.
Shipbuilding depends on highly specialised skills with long training lead times, meaning that delayed detection of shortages reduces the scope for anticipatory action. Inadequate labour market intelligence limits the ability of education and training systems to adjust provision in a timely manner and pushes workforce policy into a reactive mode. Immigration, reskilling, and apprenticeship measures are often introduced late and at higher cost, while local bottlenecks in critical occupations remain obscured by national aggregates. The result is delayed intervention, higher adjustment costs, and reduced effectiveness of workforce-related industrial policies.
Multi-actor collaboration is essential. The LeaderSHIP project, co-funded by the European Union, supports the upskilling and reskilling of the existing maritime workforce in response to emerging skills needs (LeaderSHIP, 2026[8]). A core component of the project is skills intelligence, bringing together industry, universities, vocational education and training providers, trade unions, industry associations, regional authorities and maritime clusters to develop a shared, evidence-based understanding of the sector’s evolving skills requirements. Similar approaches can be seen in the United Kingdom’s Shipbuilding Skills Delivery Group, which likewise brings together stakeholders from across industry, education, government and the wider skills ecosystem (UK Government, 2026[9]). Without stronger communication and co-ordination across the full skills value chain, it is difficult to develop a shared understanding of the sector’s real skills needs or to design effective responses.
Tracking technological, innovation, and decarbonisation progress
Shipbuilding plays a critical role in the adoption of advanced digital technologies and enabling maritime decarbonisation, yet progress in these areas remains difficult to demonstrate in a systematic and comparable way. Indicators on low- and zero-emission technologies are rarely integrated into official statistical systems. Available evidence is often confined to orderbooks, international market share or funding for flagship demonstration projects, rather than production outcomes, technological capabilities, or diffusion of innovations across shipyards and supply chains. Emerging developments, including alternative-fuelled vessels and autonomous or digitally enabled ships, are not consistently captured within existing data frameworks. Where data exists, it is often confined to paid sources, further limiting visibility into technological change across the sector.
These limitations are increasingly challenging as industrial policy places greater emphasis on digitalisation, resilience, and decarbonisation, requiring multi-dimensional indicators that capture not only output volumes but also technological complexity, innovations and environmental performance. In the absence of robust and comparable data, policymakers struggle to assess whether low- and zero-emission technologies are scaling beyond early adoption, how national shipyards compare internationally, and whether industrial and climate policies are mutually reinforcing. Innovation indicators such as patents offer a useful but incomplete lens on technological capability, particularly in areas such as propulsion systems, emissions reduction technologies, and digital maritime solutions (OECD, 2009[10]; Archibugi, 1992[11]). This can reduce their effectiveness for their use in timely, policy-relevant evidence to inform industrial transformation.
Data capabilities also vary widely across firms and regions, with smaller firms frequently lacking the digital infrastructure required for consistent reporting. At the same time, knowledge exchange remains fragmented, with information siloed across firms, clusters, and public bodies and reliance on informal networks limiting transparency and trust. Information on technology, innovation and decarbonisation progress is often collected through specific programmes rather than via systematic and comparable data frameworks.
In the United Kingdom, for example, this includes initiatives such as the UK SHORE programme, the Offshore Renewable Energy (ORE) Catapult, and Maritime UK’s annual sector report (Innovate UK, 2026[12]; ORE Catapult, 2026[13]; Maritime UK, 2022[14]). Industry clusters and associations often act as intermediaries between government and firms in these processes, highlighting a promising foundation for strengthening data collection and co-ordination in these areas.
Together, these factors constrain the development of timely, comparable, and policy-relevant evidence on shipbuilding performance.
Improving shipbuilding data should be understood as an investment in industrial policy capability. Integrating shipbuilding more consistently into national statistical systems, strengthening productivity-relevant indicators, and improving labour and skills intelligence would support more targeted and effective interventions. Embedding decarbonisation indicators into official statistics would also strengthen the alignment between industrial and climate objectives.
International collaboration is essential. Shared definitions and common analytical frameworks would improve comparability, support benchmarking, and facilitate co-ordinated policy responses. At the same time, stronger institutional frameworks for secure and transparent data sharing can help build trust and improve knowledge exchange between industry and government.
The OECD continues to advance work in this area through its publicly available statistical and analytical tools, playing a critical role in providing a harmonised and standardised framework that enables more consistent and reliable comparable analyses and benchmarking across countries. These include the STructural ANalysis (STAN), Inter‑Country Input‑Output (ICIO) and Trade in Value Added (TiVA) databases (OECD, 2025[15]; OECD, 2025[16]; OECD, 2025[17]). These databases are closely interconnected: STAN provides national data that feed into the construction of the ICIO tables, from which TiVA indicators are subsequently derived. Indicators for Building of ships and boats (ISIC 301) are available in all three datasets, enabling systematic and cross-country analysis of the shipbuilding industry.
The STAN database uses annual National Account data to compile core structural indicators for the shipbuilding industry, including output, value added, investment, and capital stock, labour input and compensation of employees. Figure 1 shows substantial cross-country differences in the value-added-to-output in shipbuilding, capturing the value created within the industry during production. Between 2010 and 2020, domestic value-added shares increased in France, Germany and Korea, while remaining broadly stable or declining slightly in Japan and Italy. STAN data therefore provides a basis for systematic cross-country analysis of industry structure, competitiveness and wage dynamics, while highlighting important structural differences across shipbuilding economies.
Ratio of value added to output (%) for 5 shipbuilding committee members, 2010 and 2020
Note: The data cover both commercial shipbuilding and the construction of military ships and vessels, as both activities fall under ISIC 3011 Building of ships and floating structures.
Source: OECD, STAN Database, 2025 edition, https://oe.cd/stan.
In addition to the structural statistics provided by STAN, the TiVA database captures the integrated nature of the shipbuilding industry by tracing the sources of value added along domestic and international supply chains. TiVA indicators are derived from ICIO data, which provides a detailed global mapping of inter‑industry flows of goods and services, enabling the analysis of production linkages, consumption, investment within and across 80 economies.
Figure 2 shows domestic value added embodied in shipbuilding gross exports, indicating the share of export value generated by domestic production. There is a decline in the domestic value-added content of shipbuilding gross exports between 2015 and 2022 across most economies, reflecting a reduced share of local content in exports. In 2022, domestic value added accounted for on average, 66% of shipbuilding exports across the OECD, with notable cross-country variation. By linking the shipbuilding production to its upstream manufacturing and service inputs, TiVA makes it possible to assess the position of a country’s shipbuilding sector within global supply chains, improving the understanding of the real value added of exports.
Domestic value added embedded in shipbuilding gross exports (%), 2015 and 2022
Note: The data cover both commercial shipbuilding and the construction of military ships and vessels, as both activities fall under ISIC 3011 Building of ships and floating structures.
Source: OECD, Trade in Value Added (TiVA) database, 2025 edition, https://oe.cd/tiva.
Beyond these core industrial and value‑chain datasets, the OECD Ocean Economy Monitor, launched in 2025, extends measurement to the wider ocean economy by combining data underlying the OECD ICIO tables with additional sources to track ocean activities across 142 coastal countries over 25 years (OECD, 2025[18]) The Monitor enhances the visibility of shipbuilding and marine equipment manufacturing through indicators such as gross value added and full‑time equivalent employment, supporting more integrated analysis of the sector within the broader economy. A key development is the availability of harmonised indicators for marine equipment manufacturing, an area where comparable data have historically been limited.
Figure 3 plots real gross value added against full-time equivalent employment for shipbuilding and marine equipment, revealing substantial cross-country differences in industry scale, labour intensity and value creation. The data can be disaggregated into shipbuilding and marine equipment manufacturing, but when the two activities are viewed together, the indicators highlight how countries with strong positions across both segments, such as China, Korea and Japan, combine very large employment bases with high levels of value added. Excluding marine equipment manufacturing would alter these relationships: productivity differences across countries would widen, especially for economies where the marine equipment industry contributes a disproportionately large share of total value added.
Average gross value added and full-time equivalents in shipbuilding and marine equipment manufacturing, 2017 and 2020
Note: The data cover both commercial shipbuilding and the construction of military ships and vessels, as both activities fall under ISIC 3011 Building of ships and floating structures.
Source: OECD Ocean Economy Monitor, 2023 edition, https://oe.cd/6mc.
Improving measurement in this area will require sustained international dialogue on how to define and operationalise indicators of industrial performance and transformation in shipbuilding. The OECD Shipbuilding Committee provides a dedicated forum for advancing common approaches to measuring key dimensions of the sector, including productivity, workforce capabilities, technological upgrading, market structure, and resilience. Structured discussions could support the development of shared definitions, methodological guidance, and experimental indicators, enhancing international comparability and enabling more meaningful benchmarking of industry performance.
Strengthening these indicators would improve the quality of evidence underpinning policy discussions on competitiveness, resilience, and structural change in shipbuilding. More robust and comparable metrics would also support better targeting, monitoring, and evaluation of industrial policies, and facilitate more informed co-ordination across countries in an increasingly interconnected and strategic industry.
Ensure systematic data availability on shipbuilding. Integrate shipbuilding consistently into national statistical data and initiate regular data collection on the sector, if not already in place. This would enable governments to monitor performance, design targeted interventions, and evaluate policy outcomes more effectively.
Advance shared definitions through international collaboration. Engage through global platforms, including the OECD Shipbuilding Committee, to improve alignment, clarity, and functionality of definitions and taxonomies, including the development of consistent sub-sector classifications for shipbuilding.
Foster collaboration on data collection and indicators. Encourage co-ordination across government bodies, industry, and statistical agencies to develop and test common metrics on key industry indicators, including those that are relevant across adjacent sectors.
Strengthen structured data collection through systematic engagement with shipbuilding stakeholders, using regular surveys and benchmarking to link firm- and occupation-level insights on skills, technology and investment with national statistics and enable earlier, more effective policy intervention.
Strengthen data infrastructure, particularly for SMEs. Provide technical support and capacity-building to improve firms’ digital and data capabilities, helping to ensure more comprehensive, reliable, and representative data coverage across the sector.
These insights are from a recent research collaboration between the OECD Shipbuilding Unit and the United Kingdom’s National Shipbuilding Office. For further information please refer to the declassified paper on 2025 Data Collection Toolkit.
OECD (2025), “The Data Collection Toolkit, Data standardisation and collection for the UK shipbuilding industry.
References
[11] Archibugi, D. (1992), “Patenting as an indicator of technological innovation: a review”, Science and Public Policy, Vol. 19/6, pp. 357-368, https://doi.org/10.1093/spp/19.6.357.
[4] Eurostat (2025), NACE Rev. 2.1 – Statistical classification of economic activities in the European Union – 2025 edition, https://ec.europa.eu/eurostat/web/products-manuals-and-guidelines/w/ks-gq-24-007 (accessed on 28 January 2026).
[2] Gourdon, K. and C. Steidl (2019), “Global value chains and the shipbuilding industry”, OECD Science, Technology and Industry Working Papers, No. 2019/08, OECD Publishing, Paris, https://doi.org/10.1787/7e94709a-en.
[12] Innovate UK (2026), UK Shipping Office for Reducing Emissions, https://iuk-business-connect.org.uk/programme/uk-shipping-office-for-reducing-emissions/.
[7] Kostat (2024), Korean Standard Industrial Classification, http://kssc.mods.go.kr:8443 (accessed on 28 January 2026).
[8] LeaderSHIP (2026), LeaderSHIP for skills, https://leadership4skills.eu/.
[14] Maritime UK (2022), State of the Maritime Nation, https://www.maritimeuk.org/state-of-the-maritime-nation/.
[6] Ministry of Internal Affairs and Communications (2023), Japan Standard Industrial Classification (Rev. 14, 2023)Structure and Explanatory Notes, https://www.soumu.go.jp/english/dgpp_ss/seido/sangyo/san14-3a.htm#e (accessed on 28 January 2026).
[16] OECD (2025), Inter-Country Input-Output tables, https://www.oecd.org/en/data/datasets/inter-country-input-output-tables.html.
[18] OECD (2025), Ocean Economy Monitor, https://www.oecd.org/en/about/programmes/ocean-economy-monitor.html.
[15] OECD (2025), Structural Analysis Database, https://www.oecd.org/en/data/datasets/structural-analysis-database.html.
[1] OECD (2025), The Data Collection Toolkit, Data standardisation and collection for the UK shipbuilding industry, https://one.oecd.org/official-document/DSTI/SBC(2025)9/FINAL/en.
[17] OECD (2025), TiVA 2025 edition, https://data-explorer.oecd.org/s/3ql.
[10] OECD (2009), OECD Patent Statistics Manual, OECD Publishing, Paris, https://doi.org/10.1787/9789264056442-en.
[13] ORE Catapult (2026), Catapult Offshore Renewable Energy, https://ore.catapult.org.uk/.
[9] UK Government (2026), National Shipbuilding Office, https://www.gov.uk/government/groups/national-shipbuilding-office#skills-delivery-group.
[3] United Nations Statistics Division (2023), ISIC, Rev. 5 Explanatory Notes, https://unstats.un.org/unsd/classifications/Meetings/UNCEISC2023/3-1_Background1_ISIC5_Exp_Notes.pdf (accessed on 28 January 2026).
[5] United States Census (2026), North American Industry Classification System, https://www.census.gov/naics/ (accessed on 28 January 2026).
Contact
Laurent Daniel (
laurentc.daniel@oecd.org)
Marina Daley (
marina.daley@oecd.org)
Hugo Vitrac (
hugo.vitrac@oecd.org)