The International Aid Transparency Initiative publishes the world’s most comprehensive record of development spending. But using it to answer basic questions — how much aid did a Pacific country receive? who is funding what? — requires navigating five systematic quality problems. This page quantifies each one from the live data and explains what the Pacific Aid Signal does about it.
When a donor runs a global or regional programme, they tag every transaction to every country mentioned in the activity — even when the money is explicitly declared for one country. A $100M programme that names 40 countries reports $100M against each country unless the publisher declares percentage splits.
Across the 14 Pacific island countries, 169,628 of 240,417 transactions (70.6%) are for somewhere else. Without correction, this creates absurdities: the US State Department appears as Tonga’s largest funder at $91 billion, when Tonga’s actual declared share of those global programmes is 0.005%.
IATI publishes historical records, not real-time data. Some funders update monthly; others have not published a new transaction in over a year. When a funder’s data goes stale, their spending disappears from any recent-period view — not because they stopped spending, but because they stopped publishing.
15 of 29 funders in the Pacific have data more than a year old. The largest is Australia Aid, the single biggest bilateral funder in 9 of 14 countries, with no transaction dated after 30 June 2025 — a gap of 468 days.
The same aid activity can appear multiple times in IATI when both the funder and the implementing partner publish it. A $50M programme reported by both the donor government and the UN agency implementing it shows up as $100M in a naïve sum.
Across the Pacific, $388M (4.1%) of $9.36B in active spend was identified as potential duplicates in 88 clusters using title matching across publishers (September 2026). This figure is indicative, not a confirmed correction. Title matching cannot distinguish genuine co-financing from duplicate reporting — if two donors contribute $10M and $20M to one programme, the real total may be $30M, not $20M. IATI provides transaction linkages that could help resolve this, and d-portal applies its own secondary-publisher rules. The clusters identify records requiring investigation, not a validated financial correction.
IATI transactions can be negative — representing refunds, adjustments, de-commitments, or corrections. These are legitimate records, but a naïve sum that ignores them will overstate spending, and a system that excludes them will miss real reductions in aid.
2,977 negative transactions appear across the 14 Pacific countries. In Nauru, negative transactions in recent periods can make the 90-day total appear negative — not because aid was withdrawn, but because a prior period’s adjustment was booked in the current window.
A funder who disbursed in the last year but has no transaction in the last 90 days is “quiet” — they are active but invisible in any recent view. This can mean their data is slightly delayed (benign), or it can mean a programme has stalled or been redirected (significant). Without flagging the gap, the 90-day picture silently omits them.
144 quiet funder-country pairs across the region. These are funders who appear in the 365-day totals but are missing from the 90-day view.
| Country | Quiet funders | Examples |
|---|---|---|
| Fiji | 22 | UN-Habitat, International Finance Corporation, Peace Corps + 5 more |
| Solomon Islands | 19 | Global Environment Facility, Department of State, United Nations Office for Project Services + 5 more |
| Papua New Guinea | 18 | Gates Foundation, Norad - Norwegian Agency for Development Cooperation, Sweden + 5 more |
| Vanuatu | 16 | International Finance Corporation, International Labour Organization (ILO), Trade and Development Agency + 5 more |
| Tonga | 13 | Japan International Cooperation Agency (JICA), AFD, Peace Corps + 5 more |
| Kiribati | 13 | Foreign Affairs, Trade and Development Canada (DFATD), Japan International Cooperation Agency (JICA), Ministry of Foreign Affairs of the Netherlands + 5 more |
| Samoa | 11 | U.S. Agency for International Development, Ministry of Foreign Affairs of the Netherlands, AFD + 5 more |
| Marshall Islands | 8 | Green Climate Fund, Department of the Interior, International Labour Organization (ILO) + 5 more |
| Palau | 8 | Department of the Interior, Peace Corps, United Nations Development Programme + 5 more |
| Tuvalu | 7 | Department of State, United Nations Capital Development Fund, World Health Organization + 4 more |
The Pacific Aid Signal applies rules to raw IATI data before publishing. These are deterministic scripts with no model calls at runtime — every rule is reproducible and auditable. Deterministic processing does not guarantee correct methods or complete coverage.
| Problem | What the Signal does | Scale |
|---|---|---|
| Misattributed transactions | Recipient-country weighting: reads declared percentage splits and excludes transactions where the money is for another country | 169,628 transactions excluded (70.6%) |
| Cross-publisher overlap | Identifies potential duplicates by title matching across publishers; flags clusters for investigation (may not distinguish co-financing from duplicate reporting) | $388M (4.1%) of $9.36B flagged as potential duplicates (indicative, not confirmed) |
| Data staleness | Per-funder data currency tracking: shows when each funder last published, flags stale data, supplements with direct source reading (DFAT procurement notices, NZ GETS tenders) | 15 funders with data >1 year old |
| Implausible amounts | Flags activities claiming >$500M lifetime with >50% attributed to a single Pacific country | Caught US State Department showing as Tonga’s largest funder at $91B |
| Negative transactions | Included in totals (they represent real refunds/adjustments) but counted separately for transparency | 2,977 across 14 countries |
IATI is actively reimagining its approach to data quality. The 2026–2030 Strategic Plan (Area D) shifts from publisher compliance metrics to user-centric data quality — asking whether the data is useful to the people trying to use it, not just whether it was published on time. An EU-funded project is building the tools for this new approach through 2026.
The problems documented on this page are exactly what that initiative aims to address. They are not abstract — they are what happens when you try to answer a simple question like “how much aid did Fiji receive in the last 90 days?” using raw IATI data. The answer without corrections is wrong by a factor of three or more.
In July 2026, Publish What You Fund tested Claude Code on IATI data and found three failure modes: hallucinations, missed double-counting, and context blindness. The Pacific Aid Signal uses a different approach: no model calls at runtime (reducing hallucination risk), cross-publisher overlap flagging, and recipient-country weighting. However, a deterministic pipeline can still contain incorrect methods or hardcoded conclusions, and the deduplication approach has not been independently validated.
Four AI tools now work with IATI data, each at a different layer: DevExplorer reads narrative documents (RAG), ChecKI checks publisher text quality, AidInsights answers natural-language queries, and the Pacific Aid Signal monitors transactions with data quality correction. The quality problems described here affect all of them differently — but they hit hardest at the transaction level, where a single misattributed activity can shift a country’s total by billions.
Every figure is computed from the live IATI data snapshot, dated 2026-10-11. The Signal reads IATI through d-portal’s transaction search, applying 60-day slices across 14 countries (d-portal caps results and does not paginate reliably). The exclusion rates, currency dates, negative counts, and quiet-funder lists are all computed deterministically at render time from the same snapshot that produces the country pages. The cross-publisher double-counting figure ($388M / 4.1%) was computed in a dedicated analysis in September 2026.
Source code: https://github.com/intexpagent-01/asa-research. The pipeline has no model calls at runtime — every correction is a deterministic rule applied to structured data.