Administrative Data Sources

1. Outcome

This Circular provides guidance on leveraging administrative data sources for the compilation of Ocean Accounts. Administrative data--records collected by government agencies and other organisations in the course of their regular operations--represent a cost-effective and often comprehensive source of information for ocean accounting, particularly for maritime activities that are subject to regulatory oversight^[1]. Such data sources include permits and licenses for fishing and aquaculture, customs and trade records, vessel registration systems, and vessel tracking data from Automatic Identification Systems (AIS) and Vessel Monitoring Systems (VMS).

The effective use of administrative data offers significant advantages for ocean accounting programmes, reducing respondent burden compared to purpose-designed surveys, providing near-complete population coverage for regulated activities, and enabling temporal and spatial granularity that would be prohibitively expensive to collect through primary data collection^[2]. However, administrative data also present unique challenges related to coverage, concepts, and quality that must be addressed through systematic quality assurance procedures.

Administrative data and purpose-designed surveys represent complementary approaches rather than substitutes. Administrative sources excel where regulatory frameworks produce comprehensive, consistent records--such as licensed commercial fishing, international trade, and vessel movements. Surveys remain necessary where administrative coverage is incomplete: subsistence and recreational fisheries, informal-sector maritime activities, and emerging ocean economy sectors not yet subject to licensing requirements. A well-designed ocean accounting programme identifies which activities are best captured through administrative sources and where targeted surveys are needed to fill gaps, using the accounting framework itself to detect coverage deficiencies through supply-use confrontation. The methodology for designing such complementary surveys is addressed in TG-4.1 Survey Methods.

Readers of this Circular will gain understanding of the main types of administrative data relevant to ocean accounting, the institutional arrangements necessary to access such data, methods for integrating administrative records with statistical frameworks, quality assurance procedures to ensure fitness for purpose, and practical workflows for transforming regulatory records into accounting entries. The guidance serves national statistical offices, fisheries management agencies, maritime authorities, and customs administrations seeking to contribute to or compile Ocean Accounts. The foundational concepts of Ocean Accounts are established in TG-0.1 General Introduction to Ocean Accounts, and the governance structures required for multi-agency data collaboration are set out in TG-0.7 Institutional Arrangements.

2. Requirements

This Circular requires familiarity with:

• TG-0.1 General Introduction to Ocean Accounts—provides foundational understanding of Ocean Accounts components and the relationship between environmental and economic accounting frameworks, including the conceptual distinctions between physical and monetary accounts that administrative data must support.

• TG-0.7 Institutional Arrangements—establishes the governance structures, data sharing agreements, and inter-agency coordination mechanisms that are prerequisites for accessing administrative data held by regulatory authorities.

Related Circulars:

• TG-1.5 OA and Fisheries Management—application of accounts to fisheries policy, including treatment of aquatic resources as environmental assets
• TG-3.3 Economic Activity Relevant to the Ocean—classification of ocean-related industries and businesses for economic accounts
• TG-3.4 Flows from Economy to Environment—recording of residuals and pressures using permit data on authorised discharges
• TG-4.1 Remote Sensing and Geospatial Data (statistical survey methodology for filling gaps in administrative coverage)—design of complementary surveys where administrative coverage is incomplete
• TG-4.2 Survey Methods (earth observation and remote sensing data for spatial attribution and vessel tracking pattern validation)—independent validation of vessel presence and activity from satellite and aerial imagery
• TG-6.7 Fisheries Accounting—integration of fisheries license data with stock assessment and catch accounts
• TG-4.6 Data Harmonisation and Interoperability—OBS_STATUS codelist conventions and SDMX metadata standards applied to administrative data quality flags (see Section 3.4.3)
• TG-6.10 Maritime Transport and Ports—use of port records and vessel tracking data for maritime accounts

3. Guidance Material

3.1 Permits and Licenses

Permit and license data provide fundamental information on the population of economic units authorised to undertake ocean-related activities. Such administrative systems are maintained by government agencies responsible for regulating access to marine resources and maritime space^[3]. The data captured through these systems support the compilation of economic accounts by identifying the universe of producers, and enable physical flow accounts by providing authorised extraction or emission levels.

In practice, the terms "permit" and "license" are used with varying specificity across jurisdictions. Some countries use "license" for the general authorisation to engage in an activity (such as commercial fishing) and "permit" for specific conditions attached to that authorisation (such as access to a particular zone or species). Other jurisdictions use the terms interchangeably. For ocean accounting purposes, both instruments serve the same function: they generate administrative records that identify economic units, authorised activities, and regulatory conditions. Compilers should document the national terminology and map permit and license categories to the relevant accounting classifications, ensuring that the scope of administrative data is clearly understood regardless of local naming conventions. This mapping is particularly important in the Pacific Islands context, where regional organisations such as the Pacific Community (SPC) and the Forum Fisheries Agency (FFA) coordinate data across jurisdictions with differing regulatory terminology.

3.1.0 Administrative Data Custodians and Multi-Agency Deduplication

An administrative data custodian is the agency with statutory authority to issue, amend, or revoke the regulatory instrument in question. Most ocean-related activities are subject to oversight by more than one custodian, and compilers must establish a clear record-authority hierarchy before integrating data across registers.

Record authority hierarchy by data type:

For multi-agency situations (for example, aquaculture operations holding a production license from the fisheries ministry, a discharge permit from the environment ministry, and a site lease from the coastal zone management authority), the issuing agency for the primary licensed activity takes precedence for record authority; secondary agencies supply supplementary fields (discharge limits, site coordinates) into the integrated record.

Deduplication decision tree: records describing the same operator across multiple registers are linked using a shared operator identifier (the tax registration number or national business identifier as the primary link key). Where the same activity appears in more than one register, the most recently updated record from the primary custodian takes precedence, with secondary-custodian fields appended where they extend rather than contradict the primary record. The formal multi-agency data-sharing agreement template is set out in TG-0.7 Institutional Arrangements.

3.1.1 Fishing Permits and Licenses

National fisheries authorities typically maintain comprehensive registers of fishing vessels and operators authorised to harvest aquatic resources within territorial waters and the exclusive economic zone (EEZ). Under the United Nations Convention on the Law of the Sea (UNCLOS), coastal States have sovereign rights to manage living resources within their EEZ, which extends up to 200 nautical miles from baselines^[4]. This jurisdictional framework underpins the licensing systems that regulate access to fisheries.

The treatment of fish and aquatic resources within the SEEA framework is detailed in TG-6.7 Fisheries Accounting, which explains how license data feed into asset accounts for natural aquatic resources. The distinction between capture fisheries (ISIC group 031) and aquaculture (ISIC group 032) is fundamental to both regulatory frameworks and accounting treatment^[5].

Key data elements captured in fishing license systems typically include:

Live-weight conversion of quota data. Quota volumes shall be converted to live-weight equivalent using the source hierarchy described in Section 3.2.1. For Pacific tuna quotas issued under FFA Vessel Day Scheme (VDS) arrangements (recorded as fishing days rather than tonnage), apply the FFA-published species-specific catch-per-vessel-day coefficients (updated annually by FFA/SPC) to convert vessel-day quotas to live-weight equivalent tonnes.^[6]

For countries with quota-based fisheries management, license data include allocated catch limits that can be compared against reported landings to assess compliance and sustainable yield. Where individual transferable quotas (ITQs) exist, records of quota trades provide additional information on the economic value of access rights^[7]. This valuation information is relevant to the compilation of monetary asset accounts as discussed in TG-3.1 Asset Accounts.

A common challenge in using license databases for accounting purposes is the presence of inactive records--vessels that remain on the register but are no longer operational. License registers may include vessels that have been decommissioned, sold outside the jurisdiction, or simply ceased fishing activity without formally surrendering their license. For accounting purposes, compilers should distinguish between the "registered" fleet (all current license holders) and the "active" fleet (vessels that reported activity during the accounting period). This distinction can be established by cross-referencing license registers with landing declarations, vessel tracking data, or annual renewal records.

Materiality threshold for inactive vessels. An inactive-vessel share exceeding 10% of the registered fleet triggers a register audit conducted jointly with the fisheries management agency, with results documented in the source register maintained under Section 3.7. Inactive vessels are retained in capital stock accounts as idle capacity, flagged with an "inactive" status attribute in the vessel register extract, consistent with SNA 2025 para. 20.14 on the treatment of idle fixed assets^[8]. They are not excluded from the fleet capital stock but are reported separately in a memorandum row to enable over-capacity assessment. See TG-3.1 Asset Accounts for the capital stock recording conventions.

Decision use cases for fishing license data:

1. Stock assessment calibration—License records establish the upper bound of fishing capacity, enabling fisheries scientists to calibrate effort estimates and detect unreported fishing when observed catches exceed licensed capacity.

2. Economic activity measurement—License fee revenues and vessel characteristics feed directly into supply-use tables for the fishing industry, supporting measurement of government revenue from natural resource access and the capital stock of fishing enterprises.

3. Spatial attribution—Authorized fishing areas in license records enable attribution of catches to specific marine zones for spatially disaggregated asset accounts, particularly important for countries with large EEZs where different zones support different fisheries.

3.1.2 Aquaculture Licenses

Aquaculture operations--the farming of aquatic organisms including fish, molluscs, crustaceans and aquatic plants--are typically subject to environmental permits that authorise the location, scale, and species cultivated^[9]. These permits capture information essential for compiling asset accounts for cultivated biological resources.

Administrative records for aquaculture commonly include:

• Site location and authorised production area (hectares or volume)
• Species authorised for cultivation
• Production capacity (tonnes per annum)
• Environmental conditions and discharge limits
• Biosecurity requirements
• Ownership and corporate structure

Aquaculture licensing data support both physical and monetary accounts. Production capacity figures provide the basis for estimating potential output, while environmental discharge limits enable recording of flows from economy to environment, including nutrient loads and organic wastes^[10]. The methodology for recording such flows is addressed in TG-3.4 Flows from Economy to Environment.

Integrated multi-trophic aquaculture (IMTA) systems present a specific accounting consideration. In IMTA operations, waste outputs from one cultured species (for example, nutrient-rich effluent from finfish cages) serve as inputs for another species (such as seaweed or filter-feeding shellfish). The net discharge to the marine environment is reduced by the amount absorbed by the co-cultured species, and permits for IMTA operations may specify both gross discharge limits and expected uptake by extractive species.

Recording IMTA flows—net entry with mandatory gross memorandum. The net recording approach for IMTA internal nutrient transfers is a practical approximation permissible where permit data explicitly cover internal recycling flows. However, SEEA CF physical flow accounts are gross by design to preserve the supply-use identity^[11], and gross flows must therefore also be recorded as a memorandum item alongside the net entry. The following two-row recording convention applies:

IMTA recording is flagged as a known area where national practice may deviate from the SEEA CF gross-recording standard; the deviation must be documented in account metadata. See TG-3.4 Flows from Economy to Environment for the authoritative SEEA CF residual flow recording convention.

Decision use cases for aquaculture license data:

1. Nutrient loading estimates—Permit discharge limits combined with production data enable estimation of nutrient contributions to coastal eutrophication, feeding into water quality accounts and SDG indicator 14.1.1 on coastal eutrophication.

2. Production monitoring—Licensed production capacity provides a benchmark against which reported output can be assessed, with systematic under-reporting indicating either data quality issues or informal sector activity requiring supplementary surveys.

3. Coastal zone planning—Spatial distribution of aquaculture licenses informs marine spatial planning by identifying areas of intensive use and potential conflicts with other activities.

3.1.3 Offshore Activity Permits

Maritime jurisdictions regulate a wide range of offshore activities beyond fishing. Table 3.1.3 below summarises the main permit categories and their accounting relevance.

Deep-sea mineral permits—two-category treatment. Deep-sea mining of polymetallic nodules, seafloor massive sulphides, and cobalt-rich crusts is an emerging category requiring a clear accounting-boundary rule:

• Category A—National jurisdiction. Permits issued by the national authority for extraction within the territorial sea, EEZ, or on the continental shelf are recorded in national mineral asset accounts under SEEA CF paras. 5.19--5.22^[15].
• Category B—The Area (ISA contracts). Exploration and exploitation contracts issued by the International Seabed Authority for extraction in the Area under UNCLOS Part XI fall outside the national accounting boundary and are excluded from national mineral asset accounts. Where the country is an ISA contractor-sponsoring state (currently including Nauru, Tonga, Kiribati, and Cook Islands), the ISA contract data may be noted as supplementary information in a memorandum table.

Decision use cases for offshore permits:

1. Residual flow accounting—Dumping permits specify authorized disposal volumes, providing direct measurement of waste flows to marine environment that populate residual flow accounts under SEEA CF.

2. Capital formation—Offshore energy permits track investment in marine renewable energy infrastructure, feeding into capital formation accounts for the ocean economy.

3. Environmental pressure indicators—Aggregation of permit data across offshore activities enables compilation of spatial pressure maps showing cumulative impacts on marine ecosystems.

3.2 Customs and Trade Data

Customs administrations generate comprehensive data on international trade in goods, including seafood products, maritime equipment, and other ocean-related commodities. These data are essential for compiling supply and use tables, physical flow accounts for traded products, and balance of payments statistics^[16].

3.2.1 Seafood Trade Statistics

Customs records for fish and fishery products provide detailed information on:

• Product classification (Harmonized System codes at 6-digit or national tariff line detail)
• Volume (tonnes) and value (FOB/CIF)
• Country of origin/destination
• Port of entry/exit
• Importer/exporter identification

The SEEA AFF recommends that physical flow accounts for fish products record imports and exports in live weight equivalent to ensure consistency with domestic production statistics^[17]. Conversion factors must be applied to transform processed product weights (fillets, frozen, canned) to the comparable live weight measure.

Live-weight conversion factor source hierarchy. The choice of conversion factor materially affects total live-weight equivalent trade volumes and the supply-use balance, particularly for seafood-exporting countries. Compilers should apply the following three-step adaptation protocol:

1. Primary—FAO Fisheries and Aquaculture Department live-weight conversion factor database (FAO 1994, updated). Search the database for the exact species (FAO ASFIS alpha-3 code) and processing form (whole, gutted, fillets, canned, etc.).
2. Secondary—ICES Working Group on Fisheries Acoustics, Science and Technology (WGFAST) technical report series, used for processed and value-added products not adequately covered by FAO factors. National fisheries institute conversion factors may also be applied at this tier, provided the source name and vintage year are recorded in the source register (Section 3.7).
3. Default—nearest taxonomic group factor from the FAO database, applied where neither a species-specific FAO factor nor a national factor is available. Estimates produced at this tier must be flagged as provisional in account metadata.

Worked example. A national customs system records 1,000 tonnes (net product weight) of canned skipjack tuna exports. Applying the FAO factor of 1.9 for canned tuna (drained-weight basis), the live-weight equivalent for the physical export account is 1,000 × 1.9 = 1,900 tonnes.

This conversion workflow is consistent with the parallel live-weight conversion of quota data described in Section 3.1.1; both pathways must apply the same factor source for a given species and processing form to preserve supply-use consistency.

For island nations and coastal States where fisheries are economically significant, customs data serve multiple accounting purposes:

Supply-use balancing—Exports and imports provide the external trade components necessary to balance domestic production against consumption and intermediate use^[18]. The integration of trade data with production data is discussed in TG-6.7 Fisheries Accounting.

Trade in fish caught by foreign vessels—When foreign-flagged vessels harvest fish in a country's EEZ under access agreements, the transhipment or landing of catches may be recorded in customs statistics, supporting the distinction between catches from national waters versus catches by national fishing vessels^[19]. This distinction is particularly important for Pacific Island Countries where distant-water fishing nations operate under access agreements.

For Pacific Island Countries, reconciliation between national customs records and regional databases maintained by the Forum Fisheries Agency (FFA) requires systematic attention. FFA compiles catch and effort data reported under regional fisheries management arrangements, which may differ from national customs records due to differences in timing (landing date versus export date), valuation (access fees versus market price), and coverage (transhipments at sea versus port landings). Compilers should establish regular data confrontation between customs and FFA sources, documenting the reconciliation procedures and any systematic differences. The FFA's Regional Fisheries Surveillance Centre also maintains vessel day scheme data that can cross-validate customs records for purse seine fisheries. Institutional arrangements for accessing FFA data are facilitated through national fisheries agencies and should be formalised under the frameworks described in TG-0.7 Institutional Arrangements.

Illegal, unreported and unregulated (IUU) fishing indicators—Comparison of import volumes with reported exports from origin countries can reveal discrepancies indicative of unreported catches or trade in IUU-sourced products^[20]. SDG indicator 14.6.1 tracks progress in combating IUU fishing, which can be supported by trade data analysis.

Decision use cases for customs trade data:

1. Supply-use balancing—Customs data provide the import and export components of seafood supply-use tables, enabling detection of statistical discrepancies that may indicate unreported production or consumption.

2. IUU fishing detection—Trade flow analysis comparing bilateral import-export data identifies anomalies consistent with illegal fishing, supporting SDG 14.6.1 monitoring.

3. Value chain analysis—Product-level trade data reveal the structure of seafood value chains, showing which species are exported fresh versus processed domestically, informing industrial development policy.

3.2.2 Maritime Equipment and Inputs

Customs data also capture trade in goods used as inputs to ocean industries:

• Fishing vessels and parts (relevant to capital formation accounts)
• Marine engines and propulsion equipment
• Fishing gear (nets, lines, traps)
• Aquaculture equipment (cages, feeders, aeration systems)
• Fuel and lubricants (with marine-specific customs categories)
• Ice and refrigeration equipment

These data support the compilation of intermediate consumption and capital formation components of ocean economy supply-use accounts.

3.2.3 Port Statistics and Maritime Transport

Port authorities and customs administrations generate data on vessel movements and cargo throughput that are central to measuring maritime transport activity:

• Vessel arrivals and departures by flag state, vessel type, and tonnage
• Cargo handled (tonnes, container TEUs, passengers)
• Port service revenues

These data feed into both national accounts (transport sector output) and physical flow accounts (material throughput through maritime logistics). Detailed guidance on compiling maritime transport accounts is provided in TG-6.10 Maritime Transport and Ports.

3.3 Vessel Tracking (AIS/VMS)

Vessel tracking systems provide spatially explicit data on maritime activity that significantly enhance the capacity to attribute economic activity and environmental pressures to specific marine locations. Two primary systems are relevant. All inter-agency data sharing agreements required by this section should be established under the framework in TG-0.7 Institutional Arrangements.

3.3.1 Automatic Identification System (AIS)

The Automatic Identification System is an automatic tracking system mandated by the International Maritime Organization (IMO) for vessels engaged in international voyages and for all passenger vessels, cargo vessels of 300 gross tonnage and above, and fishing vessels above certain size thresholds (varying by jurisdiction)^[21]. AIS transponders broadcast vessel identity, position, course, and speed at regular intervals.

Key data elements from AIS include:

AIS data, now widely available through satellite reception and coastal base stations, enable:

Fishing effort estimation—Analysis of vessel tracks can identify fishing behaviour based on speed and movement patterns, providing estimates of fishing effort by area that complement or substitute for vessel logbook data^[22]. This spatial effort data supports the compilation of catch accounts by marine zone.

Spatial attribution—Catches can be attributed to specific marine zones (territorial sea, EEZ zones, high seas) based on vessel positions during fishing activity, supporting spatially explicit asset accounts. This aligns with the spatial framework described in TG-2.2 Macro-economic Dependencies on Ocean Ecosystems.

Fleet characterisation—Analysis of active vessel populations, activity levels, and spatial patterns provides information on the structure of fishing industries beyond what license registers capture^[23].

Environmental pressure mapping—Combining vessel activity data with emission factors enables spatial mapping of pressures from shipping emissions, underwater noise, and collision risk with marine mammals. These pressure indicators feed into TG-3.4 Flows from Economy to Environment.

A significant limitation of AIS data is gaps caused by vessels that disable their transponders--so-called "dark" vessels--or operate in areas with limited satellite or terrestrial receiver coverage. AIS gaps may be intentional (vessels seeking to conceal unauthorised fishing activity) or unintentional (equipment failure, signal interference, or gaps in satellite coverage in remote ocean areas).

Tiered AIS gap decision framework. Compilers should apply the following tiered method, choosing the tier based on the duration and pattern of the observed gap:

Materiality threshold. Where gap-adjusted fishing effort exceeds 3% of unadjusted total effort, a mandatory quality note must accompany the published account.

Worked example. A purse-seiner experiences a 5-day AIS gap in a zone with documented 70% expected satellite coverage. Tier (b) statistical estimation yields:

adjusted vessel-days = AIS-observed vessel-days / 0.70

where "AIS-observed vessel-days" is the count of days with valid AIS position fixes received within the reference zone during the gap-adjusted period.

with a ±15% uncertainty range documented in metadata to reflect typical variance in satellite-AIS coverage models.

Independent validation of vessel presence in areas with suspected AIS gaps can be obtained from remote sensing data; see TG-4.2 Survey Methods for earth observation methods supporting AIS validation.

3.3.2 Vessel Monitoring System (VMS)

Vessel Monitoring Systems are fisheries-specific tracking systems required by many fisheries management authorities for licensed fishing vessels. Unlike AIS, VMS data are typically transmitted only to fisheries management agencies and are not publicly available. VMS systems often require more frequent position reporting than AIS (commonly every 1-2 hours, or more frequently during fishing activity).

VMS data provide fisheries managers with:

• Verification that vessels are operating in authorised zones
• Records of fishing effort by location for quota management
• Evidence for enforcement actions against zone violations

For ocean accounting purposes, VMS data--where accessible under data sharing arrangements--provide higher-quality information on fishing vessel activity than AIS, particularly for smaller vessels not subject to AIS carriage requirements^[24].

VMS access fallback hierarchy. In many jurisdictions VMS data are classified as law-enforcement sensitive and fisheries agencies are legally constrained in sharing them with statistical offices. Compilers should pursue the following access hierarchy:

1. Individual vessel VMS microdata under inter-agency data sharing agreement—preferred.
2. Aggregated VMS effort grids from the fisheries agency—accepted for spatial attribution accounts (zone-level effort totals) but not for individual vessel effort analysis or capital stock accounts.
3. AIS-only compilation with documented VMS non-availability, including a stated uncertainty range on effort estimates recorded in account metadata.

Model confidentiality clause for VMS data sharing agreements. The following clause is recommended for insertion into inter-agency agreements:

VMS data transferred to the [national statistical office] shall be: used exclusively for statistical compilation purposes; stored in a secure, access-controlled environment; not disclosed at the individual vessel level in any published account output; and subject to the same statistical confidentiality provisions as individual tax records.

3.3.3 Integration with Other Data Sources

The value of vessel tracking data for ocean accounting is maximised when integrated with other administrative and statistical sources:

• Vessel registers link tracking data to vessel characteristics, ownership, and license conditions
• Landing declarations link fishing activity locations to catch species and volumes
• Customs data complete the picture of international trade flows
• Business registers enable classification of vessel operators within standard industry classifications

Such integration requires unique vessel identifiers (IMO number, national registration number, MMSI) that are consistently applied across data sources. The development of a unified vessel identifier system is recommended as part of the institutional arrangements for ocean accounting.

Small vessel identification (sub-IMO fleet). The IMO ship identification number scheme covers vessels of 100 gross tonnage and above in international service, and the voluntary IMO fishing vessel numbering scheme covers vessels above certain thresholds; the bulk of artisanal and small-scale fishing vessels (typically under 12 m length overall or under 100 GT) fall outside both schemes. For these vessels:

• National vessel register numbers serve as the primary identifier where IMO numbers are absent. NSOs should work with fisheries agencies to assign permanent unique identifiers to all licensed vessels during register digitisation. The FAO Vessel Identification and Automated Sequencing (VIAS) scheme is recommended as a proven model for national identifier assignment.
• AIS-based linking is not feasible for sub-12 m vessels (which are not legally required to carry AIS transponders). VMS matching—where available under the access hierarchy in Section 3.3.2—or landing declaration matching (vessel name + departure port + departure date) must serve as the primary integration mechanism for small-scale fleets.
• Unmatched small-vessel landing records should be aggregated to species-zone-period totals rather than discarded, preserving total catch coverage in SEEA AFF physical flow accounts.

Figure 4.3.1: Integration of administrative data sources through common vessel identifiers

Decision use cases for vessel tracking data:

1. Effort-based catch estimation—For data-poor stocks lacking comprehensive landing records, AIS/VMS-derived fishing effort by zone can be combined with catch-per-unit-effort estimates to produce spatially disaggregated catch estimates.

2. MPA compliance monitoring—Vessel tracking data enable detection of incursions into marine protected areas, supporting ecosystem condition accounts by quantifying fishing pressure in protected zones.

3. Emissions attribution—Combining vessel tracks with engine characteristics and speed-dependent emission factors enables spatial attribution of shipping emissions to coastal zones and high-traffic areas.

3.4 Administrative Data Compilation Workflow

Transforming administrative records from their source format into account-ready data requires systematic procedures. This section presents a general compilation workflow applicable across different administrative sources.

3.4.1 Pre-compilation Assessment

Before initiating compilation, compilers should assess:

Data availability—Which administrative systems exist, which agencies hold the data, and under what legal authority can the data be accessed. A systematic inventory of administrative sources relevant to ocean accounting should be prepared as part of the institutional arrangements described in TG-0.7 Institutional Arrangements.

Data scope and coverage—What population of economic units or activities is covered by the administrative system, and what known gaps exist. For example, a fishing license register covers commercial fishing but typically excludes subsistence fishing and recreational fishing.

Update frequency and timeliness—How often are administrative records updated, and what lag exists between the reference period and data availability. Port statistics may be available monthly with a 2-month lag, while annual fishing license renewals provide data only once per year.

Data format and structure—Whether data are available in machine-readable formats (databases, spreadsheets) or only as paper records or PDFs requiring manual extraction.

3.4.2 Data Access and Extraction

The framework for formal data sharing agreements—covering scope, frequency, quality standards, confidentiality regimes (statistical confidentiality, commercial sensitivity, and personal data protection), exchange formats, and dispute resolution—is set out in TG-4.7 National Data Coordination Architectures §3.2; that guidance applies directly to agreements with fisheries departments, customs administrations, and port authorities.

Once data sharing arrangements are established, extraction procedures should be documented: database queries or API calls used to extract records, file naming and versioning conventions, storage locations and backup procedures, and any manual interventions required.

3.4.3 Data Cleaning and Validation

General data cleaning and validation procedures—covering missing values, inconsistent formats, duplicate records, out-of-scope records, and implausible values—follow the classify-map-validate-integrate workflow in TG-4.6 Data Harmonisation and Interoperability §3.5 (Phase 3: Validate). The documentation requirements for quality assurance procedures (automated rules, manual review protocols, imputation decisions, and adjustments) are set out in TG-0.7 Quality Assurance Principles.

Critical fields and imputation rules—administrative data. For ocean accounting, "critical fields" are defined as: vessel identifier, catch volume (tonnes), catch species (FAO ASFIS alpha-3 code), fishing zone code, and reporting period (year or quarter). Imputation is acceptable for critical fields only where record-level missingness does not exceed 5% of records within a species-zone-period cell. Above this threshold the data source is rated as not fit for direct compilation, and supplementary estimation methods (e.g., sample survey expansion) must be applied instead. The default imputation method is nearest-neighbour hot-deck imputation, with donor pools defined by matching on vessel class, season, and fishing zone—the three variables most predictive of catch composition. All imputed values in account output tables must carry an OBS_STATUS attribute of "E" (estimated), consistent with the SDMX OBS_STATUS codelist and the conventions of TG-4.6.

3.4.4 Classification Mapping

The concordance development process—structural comparison, conceptual alignment, mapping relationships, documentation, and validation—and the management of temporal concordances across ISIC and CPC revisions are covered in TG-4.6 Data Harmonisation and Interoperability §3.4. The following example illustrates the application of that concordance process to fisheries administrative categories.

Example: Fishing license type to ISIC mapping

3.4.5 Account Integration

Once administrative data are cleaned and classified, they can be integrated into account tables. Integration procedures include:

Business register matching—Linking license holders or permit applicants to business register entries using business identifiers (tax numbers, registration numbers). This enables consistent industry classification and aggregation with other business statistics.

Temporal alignment—Aligning administrative reference periods (license validity periods, customs clearance dates) with accounting periods (typically calendar years).

Unit conversion—Converting administrative units (license counts, permit capacities) to accounting units (number of enterprises, production volumes, capital stock values).

Aggregation and tabulation—Aggregating individual administrative records to the level of account tables (industry totals, product categories, geographic zones).

3.4.6 Compilation Documentation

Compilation documentation requirements—covering data sources, cleaning procedures, classification mappings, assumptions, known limitations, and comparisons with previous rounds—follow the general quality documentation framework in TG-0.7 Quality Assurance Principles and the metadata standards in TG-4.6 Data Harmonisation and Interoperability §3.6. The source register template in Section 3.7.1 below provides the administrative-data-specific documentation format for recording custodian details, legal access basis, and quality ratings for each administrative source.

3.5 Worked Example: Fisheries Licensing Data for Catch Accounts

This section presents a synthetic worked example illustrating how fishing license data can be integrated with catch reporting data to populate physical flow accounts for capture fisheries, and how the same administrative data are extended to populate monetary production accounts. The example demonstrates the complete workflow from administrative records to account entries.

Context

The national fisheries authority maintains a database of commercial fishing licenses. Each license record includes vessel identification, authorized fishing areas, target species, and gear types. Vessel operators are required to submit monthly catch reports specifying species caught, volumes, and fishing locations. The national statistical office seeks to compile annual physical flow accounts for capture fisheries showing catch by species and marine zone, and to extend these into monetary supply-use entries.

Step 1: Data Access

A formal data sharing agreement is established between the statistical office and fisheries authority, specifying quarterly transfer of:

• License register extract (all active licenses for the accounting year)
• Catch reports database (all submitted reports for the accounting year)
• Vessel register (vessel characteristics for all licensed vessels)

Data are transferred as CSV files via secure file transfer protocol.

Step 2: Data Extraction

License register extract (sample records):

Catch reports database (sample records):

Step 3: Data Cleaning and Validation

Check 1: Coverage completeness

Licensed vessels with no catch reports: 12 vessels (8% of register)
Catch reports from unlicensed vessels: 0 (good)

Interpretation: 12 vessels licensed but not reporting catches may be inactive or non-compliant. Flag for follow-up with fisheries authority.

Check 2: Spatial compliance

Catch reports from zones outside licensed areas: 3 reports (0.5% of total)

Interpretation: Small number of zone violations detected. These catches are included in accounts but flagged for enforcement follow-up.

Check 3: Temporal alignment

Late reports (submitted >60 days after reference month): 47 reports (8% of total)

Interpretation: Most reports submitted on time. Late reports are included in annual accounts but noted for quality documentation.

Check 4: Plausibility

Reports with catch > vessel hold capacity: 2 reports
Reports with implausible coordinates: 5 reports

Interpretation: 7 reports flagged for manual review. After consultation with fisheries authority, 5 corrected, 2 excluded as data errors.

Step 4: Classification Mapping

Species code to ISSCAAP mapping (International Standard Statistical Classification of Aquatic Animals and Plants):

Zone code to marine spatial unit mapping:

Step 5: Account Integration—Physical Flow Accounts

Aggregation of catch reports to annual totals:

Link to business register for industry classification:

Using Vessel_ID → Operator_Name → Business_Register_ID matching:

• 138 licensed vessels matched to 47 fishing enterprises
• 12 vessels with no catch reports correspond to 8 enterprises (likely inactive)
• All enterprises classified as ISIC 0311 (Marine fishing)

Populate physical flow account:

Step 6: Account Integration—Monetary Production Accounts

The same administrative records extend into monetary supply-use accounts through three pathways: output at basic prices, license fees as government revenue, and the vessel register as a capital stock source.

(a) Output at basic prices. Landing-volume records from the catch declarations are linked to ex-vessel price data drawn from fish market transaction records (or, where unavailable, fisheries agency price surveys). The SNA 2025 basic-price convention (output measured as the value received by the producer, excluding VAT and other taxes on products but including subsidies on production)^[25] is applied. For example, 1,247 t of tuna at an ex-vessel price of USD 3,400 per tonne yields output at basic prices of USD 4.24 million for tuna; equivalent calculations are performed for each species-zone cell and summed to populate the production row of the fishing industry supply-use column.

(b) Government revenue from natural resource access. Annual license fee receipts recorded in the fisheries agency revenue system are mapped to the "government revenue from natural resource access" row of the ocean economy supply-use table. License fees for access to natural aquatic resources are recorded as rent payments for the use of natural assets (SNA/SEEA transaction code D.45), consistent with SEEA EA paras. 5.198--5.199, which classify access-rights fees for living natural resources as resource rent rather than taxes on production. Compilers should ensure consistency with the national accounts treatment of similar resource-rent flows.

(c) Capital stock from the vessel register. Gross registered tonnage (GRT) and vessel age are extracted from the vessel register. Age-based depreciation is applied using straight-line depreciation over the assumed vessel service life (typically 20--30 years for steel-hulled commercial fishing vessels), yielding a net written-down value that is recorded in the fishing fleet asset account. Inactive vessels identified under the 10% threshold in Section 3.1.1 are reported in a memorandum row separately from active fleet capital stock.

The full supply-use table structure into which these entries are integrated is described in TG-3.3 Economic Activity Relevant to the Ocean.

Step 7: Quality Assessment and Documentation

Coverage estimate:

Licensed vessels: 150
Vessels reporting catches: 138 (92%)
Estimated coverage of commercial fishing: 90-95%

Known gaps:

• Subsistence fishing (not licensed): estimated 200-300 tonnes annually based on household surveys
• Recreational fishing (licensed but not included in commercial statistics): estimated 50-100 tonnes annually
• Unreported commercial fishing (IUU): unknown, likely <5% based on enforcement records

Data quality flags:

• License register: Good quality, updated annually, 92% reporting rate
• Catch reports: Moderate quality, 8% late submission, 0.5% spatial violations, <1% plausibility issues
• Classification: Standard ISSCAAP codes used, consistent with international reporting

Comparability with previous years:

• 2022 total catch: 2,156 tonnes
• 2023 total catch: 2,283 tonnes (6% increase)
• Change consistent with license register growth (5 additional licenses issued in 2023)

Integration with other accounts:

This physical catch account feeds into:

• Fisheries asset accounts (extraction component, see TG-6.7 Fisheries Accounting)
• Supply-use tables (domestic production of seafood products, see TG-3.3 Economic Activity)
• Trade accounts (when combined with customs export data, see Section 3.2)

Lessons from the Example

1. Integration requires unique identifiers—Vessel_ID enabled linking across license register, catch reports, and business register; consistent identifiers are a prerequisite.

2. Coverage gaps and data confrontation—Some licensed vessels will not report; cross-checking against licensed zones and vessel capacities identifies implausible records. Document known gaps for users.

3. Mappings and documentation must be maintained—Species and zone codes require translation to standard classifications, and full source documentation is essential for fitness-for-use assessment and future revision.

3.6 Quality Assurance for Administrative Data

Administrative data are collected for purposes other than statistical production, which introduces quality considerations that must be addressed through systematic evaluation and, where necessary, adjustment^[26]. The SEEA and SNA frameworks emphasize that data quality should be assessed and documented as part of the compilation process^[27].

3.6.1 Coverage and Completeness

Administrative data coverage depends on the scope of regulatory requirements:

Under-coverage occurs when economic units or activities fall outside the regulatory framework. For example:

• Subsistence and recreational fishing may not require licenses
• Small vessels may be exempt from AIS carriage requirements
• Informal sector activities may not be captured in business registers

Over-coverage due to inactive license holders or non-operational vessels is addressed through the materiality threshold and register audit procedures in Section 3.1.1. Quality assurance procedures should assess coverage against benchmark sources (censuses, surveys) and apply adjustments for known gaps. The SEEA AFF notes that illegal, unreported and unregulated (IUU) fishing should in principle be included in catch statistics, but in practice this is difficult and may require model-based estimation^[28].

Tiered IUU estimation approach. Compilers should apply the following tiered methodology to produce IUU adjustment estimates:

1. Primary—AIS dark-vessel analysis. Where national or regional surveillance data are available, use AIS dark-vessel analysis (Global Fishing Watch or equivalent national system) to identify non-reporting periods and estimate unreported effort.
2. Secondary—catch reconstruction. Where catch reconstruction data exist (e.g., Sea Around Us national reconstruction series), apply the reconstruction methodology to produce an IUU adjustment estimate and reconcile against reported landing declarations.
3. Default—regional governance proxy. For data-poor contexts lacking both surveillance and reconstruction data, apply a default IUU adjustment factor of 15--30% of reported catch, calibrated to regional enforcement-capacity proxies (e.g., OECD/FAO Fisheries Outlook governance indicators), and flag the estimate as a modelled default.

Regardless of tier, IUU estimates must be: (a) labelled as "modelled—IUU adjustment" in account tables; (b) published with explicit uncertainty intervals; (c) recorded in a separate account row from directly observed catch data, not merged into reported totals. SDG indicator 14.6.1 addresses the complementary trade-based dimension of IUU detection discussed in Section 3.2.1.

For small-scale and artisanal fisheries that are often under-represented in administrative data, complementary survey methods may be required, as discussed in TG-4.1 Remote Sensing and Geospatial Data. Specific methods for estimating the contribution of the informal sector to fisheries output include:

• Frame surveys—Periodic enumeration of landing sites, vessels, and fishing gear to establish the population of informal operators. Frame surveys are particularly effective in the Pacific Islands context where artisanal fisheries are concentrated at known landing points. The survey establishes a sampling frame from which regular catch monitoring can be conducted.
• Catch assessment surveys—Systematic sampling of landings at selected sites over defined periods, with results raised to the total population using frame survey estimates. The FAO recommends stratified sampling by landing site, vessel type, and season to capture the variability inherent in small-scale fisheries^[29].
• Household surveys—Labour force surveys or household income and expenditure surveys that include questions on fishing activity, catch quantities, and disposal (sale, subsistence consumption, barter). These surveys capture the full population of fishing households, including those not registered in any administrative system.
• Key informant estimation—For very small-scale activities, community-level estimates from fisheries officers or village leaders can provide plausibility bounds when more rigorous methods are not feasible.

Compilers should document the methods used to estimate informal sector contributions and the associated uncertainty, and flag the resulting estimates as modelled rather than directly observed.

3.6.2 Conceptual Alignment

Administrative concepts may not align with statistical definitions:

Timing differences—Administrative records may be dated by registration rather than occurrence (e.g., landing date versus catch date)

Valuation differences—Customs values (CIF/FOB) may differ from the transaction prices required for national accounts

Classification differences—Administrative product codes may not correspond to statistical classifications (CPC, HS)

Geographic scope—Jurisdictional boundaries for regulation may differ from accounting territories

Conceptual alignment requires documentation of differences and systematic bridging procedures that transform administrative data to statistical frameworks^[30].

Bridging customs values to basic prices. SNA supply-use tables require flows valued at basic prices, while customs records report CIF (imports) or FOB (exports) values. The following bridging chain applies:

Imports (from CIF customs value to basic price):

Exports (from FOB customs value to producer basic price):

Numerical example (frozen tuna fillet imports):

CIF value                                    USD 2.00 million
- International freight and insurance        USD 0.15 million
= FOB value at border                        USD 1.85 million
- Domestic wholesale margin                  USD 0.12 million
- Taxes on products less subsidies           USD 0.05 million
= Basic price                                USD 1.68 million

Compilers should obtain country-specific trade margin coefficients and product-tax rates from the national accounts unit responsible for the published supply-use tables rather than fabricating them locally. The bridging procedure follows SNA 2025 Chapter 15 (Supply and use tables), paras. 15.36--15.42, and IMTS 2010 Chapter 4 on valuation of merchandise trade^[31].

3.6.3 Data Confrontation

The general data confrontation framework—internal consistency checks, cross-source reconciliation, benchmark comparison, and physical plausibility assessment—is covered in TG-4.6 Data Harmonisation and Interoperability §3.5 (Phase 3: Validate). For administrative data specifically, confrontation of landing declarations against customs export records and vessel-capacity range checks against catch reports are the most productive cross-source checks; these are illustrated in the worked example in Section 3.5.^[32][33]

3.6.4 Temporal Consistency

Time series analysis of administrative data should identify:

• Breaks in series due to changes in regulatory scope or concepts
• Changes in reporting compliance over time
• Effects of digitisation or administrative reforms on data availability

Where discontinuities exist, back-casting or adjustment procedures may be required to produce consistent time series for accounting purposes.

3.6.5 Institutional Arrangements for Data Access

Access to administrative data for statistical purposes requires legal authority, data sharing agreements, secure transfer infrastructure, and confidentiality protocols.^[34] Template agreements and the full governance framework are in TG-0.7 Institutional Arrangements and TG-4.7 National Data Coordination Architectures.

3.7 Upward Connections to Account Uses

Administrative data flow into multiple components of Ocean Accounts, supporting a wide range of analytical uses. Understanding these upward connections helps compilers prioritize data development efforts and communicate the value of administrative data integration to custodian agencies. Table 3.7.1 maps the main administrative sources to the account components they populate and the policy uses they support.

Table 3.7.1: Administrative data sources, account components, and policy uses

These connections demonstrate that administrative data serve multiple account purposes simultaneously. For example, fishing license data feed into both asset accounts (measuring the capital stock of the fishing fleet) and pressure accounts (measuring fishing effort by zone). This multiplicity means that investments in improving administrative data quality generate benefits across multiple account domains.

Compilers should maintain a register of administrative sources showing which account components depend on each source, the update frequency required, and the priority for quality improvement. This register supports dialogue with custodian agencies, demonstrating the value of their data systems for national statistical programmes and enabling coordinated improvements in data infrastructure.

3.7.1 Administrative Source Register—Minimum-Fields Template

To support comparability across countries and regional benchmarking, the source register should capture the following ten minimum fields for each administrative source:

Example completed row—national fishing license register:

The source register should be maintained under the governance framework set out in TG-0.7 Institutional Arrangements and reviewed at each annual inter-agency data-governance meeting.

4. Summary

Administrative data sources provide essential inputs for Ocean Accounts, offering comprehensive coverage of regulated maritime activities at relatively low cost. These three data categories collectively describe the population, scope, flows, and activity patterns of ocean-related maritime activity. Effective use of these data sources requires attention to quality assurance, conceptual alignment with statistical frameworks, and institutional arrangements for data sharing. The accounting framework itself provides a powerful mechanism for confronting data from multiple sources and identifying inconsistencies that can then be resolved through consultation with data custodians.

National statistical offices and ocean accounting agencies should develop systematic programmes to identify, access, and integrate administrative data sources, working collaboratively with regulatory authorities to improve data quality and expand coverage over time. The compilation workflow presented in Section 3.4 and the worked example in Section 3.5 provide practical guidance for transforming administrative records into account-ready data—including the physical-to-monetary extension—while Section 3.7 demonstrates the multiple account uses that administrative data support and provides a minimum-fields template for the source register that anchors inter-agency data governance.

Administrative data represent a foundation for ocean accounting, particularly in countries where statistical capacity is limited and survey programmes are costly. By building strong partnerships with fisheries authorities, maritime agencies, customs administrations, and other custodians of administrative data, statistical offices can compile comprehensive Ocean Accounts that support evidence-based ocean management and contribute to SDG 14 monitoring and reporting.

5. Acknowledgements

This Circular has been approved for public circulation and comment by the GOAP Technical Experts Group in accordance with the Circular Publication Procedure.

Authors: [To be confirmed]

Reviewers: [To be confirmed]

Footnotes