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IATI data quality in the Pacific

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.

240,417 transactions read (365 days)
70.6% misattributed to wrong country
15 funders with stale data
2,977 negative transactions

Problem 1: recipient-country misattribution

high impact

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%.

71% excluded
Excluded: tagged here, money elsewhere (169,628)
Retained: money genuinely for this country (70,789)
Percentage of transactions excluded (tagged here, actually elsewhere) Palau91% (8,290 of 9,099)Marshall Islands91% (11,402 of 12,540)Nauru90% (9,133 of 10,135)Tuvalu89% (13,277 of 14,951)Niue86% (10,348 of 12,017)Samoa83% (13,160 of 15,877)Tonga82% (13,120 of 15,928)Micronesia (Fed. States)78% (8,007 of 10,265)Kiribati76% (13,101 of 17,147)Vanuatu74% (11,230 of 15,215)Solomon Islands67% (13,882 of 20,657)Cook Islands65% (7,450 of 11,406)Papua New Guinea52% (18,801 of 36,146)Fiji47% (18,427 of 39,034)
Signal correction: reads the declared recipient-country percentage on every transaction. When a transaction declares that 0.005% of the spend is for this country, only that share is counted. When no percentage is declared, the transaction is included at face value (the publisher did not say it was for elsewhere).

Problem 2: data staleness

high impact

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.

Days since last transaction (among funders with stale data) Australia Aid468 days ($9.5B)Australian Aid2294 days ($1.2B)UN Pooled Funds1380 days ($467M)AidData4666 days ($446M)Green Climate Fund389 days ($438M)World Bank Trust Funds1990 days ($331M)Millennium Challenge Corpora…2598 days ($302M)United Nations Capital Devel…2110 days ($120M)Global Green Growth Institut…648 days ($27M)International Fund for Agric…1234 days ($25M)Department of Defense2475 days ($19M)Foreign Affairs, Trade and D…844 days ($9M)
Signal correction: tracks per-funder data currency across all 14 countries (see data freshness heatmap). For Australia, supplements stale IATI data by reading DFAT’s business notifications directly — catching events like the Strongim Ekonomi managing-contractor notification for Solomon Islands the same afternoon it was published, while DFAT’s IATI data for Solomon Islands was already 15 months old.

Problem 3: cross-publisher double-counting

medium impact

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.

Signal approach: identifies potential duplicate activities by matching publisher identifiers, titles, and amounts across publishers. Flags clusters for investigation. The method is documented in the pipeline code but has not been independently validated.

Problem 4: negative transactions

context needed

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.

Negative transactions (refunds, adjustments, corrections) Fiji1,123Papua New Guinea790Solomon Islands247Vanuatu158Micronesia (Fed. States)121Samoa99Kiribati86Nauru67Marshall Islands59Cook Islands56Tonga54Palau50Tuvalu44Niue23
Signal correction: includes negative transactions in totals (they represent real adjustments) but counts them separately. When a country’s 90-day total is negative, the country page explains why.

Problem 5: quiet funders

medium impact

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.

CountryQuiet fundersExamples
Fiji22UN-Habitat, International Finance Corporation, Peace Corps + 5 more
Solomon Islands19Global Environment Facility, Department of State, United Nations Office for Project Services + 5 more
Papua New Guinea18Gates Foundation, Norad - Norwegian Agency for Development Cooperation, Sweden + 5 more
Vanuatu16International Finance Corporation, International Labour Organization (ILO), Trade and Development Agency + 5 more
Tonga13Japan International Cooperation Agency (JICA), AFD, Peace Corps + 5 more
Kiribati13Foreign Affairs, Trade and Development Canada (DFATD), Japan International Cooperation Agency (JICA), Ministry of Foreign Affairs of the Netherlands + 5 more
Samoa11U.S. Agency for International Development, Ministry of Foreign Affairs of the Netherlands, AFD + 5 more
Marshall Islands8Green Climate Fund, Department of the Interior, International Labour Organization (ILO) + 5 more
Palau8Department of the Interior, Peace Corps, United Nations Development Programme + 5 more
Tuvalu7Department of State, United Nations Capital Development Fund, World Health Organization + 4 more
Signal correction: every country page lists quiet funders by name, with the date of their last recorded transaction, so a reader can distinguish between a publication delay and a genuine programme change.

What the Signal does about it

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.

ProblemWhat the Signal doesScale
Misattributed transactionsRecipient-country weighting: reads declared percentage splits and excludes transactions where the money is for another country169,628 transactions excluded (70.6%)
Cross-publisher overlapIdentifies 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 stalenessPer-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 amountsFlags activities claiming >$500M lifetime with >50% attributed to a single Pacific countryCaught US State Department showing as Tonga’s largest funder at $91B
Negative transactionsIncluded in totals (they represent real refunds/adjustments) but counted separately for transparency2,977 across 14 countries

Why this matters now

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.

The numbers on this page

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.