## Instilling Trust through Statistics

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**Canonical URL:** [Instilling Trust through Statistics](https://www.imf.org/en/publications/fandd/issues/2025/12/point-of-view-instilling-trust-through-statistics-bert-kroese)

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## Bibliographic details
- Authors: BERT KROESE
- Published: November 18, 2025

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### Background and illustrative cases
- Argentina in the mid-2000s: official inflation figures diverged from independent estimates; by 2007 private analysts pegged inflation as much as triple the officially reported rate, leading to a collapse in credibility of the national statistics office, capital flight, and impaired policymaking.
- Nigeria: waited 20 years to rebase its national accounts and in 2010 announced its economy was almost 60 percent bigger than previously estimated, illustrating how delayed rebasing can dramatically change the economic picture.

### Mounting challenges facing NSOs
- Declining survey response rates: the response rate to the UK’s Labour Force Survey dropped below 15 percent in 2023, prompting a temporary suspension of official releases underpinning employment estimates.
- Data environment complexity: proliferation of data from social media, smart devices, and private sources, enabled by surging computing power and artificial intelligence, increases opportunities for misleading or fabricated results; large language models “hallucinate” and can produce inaccurate but plausible economic figures even when given authoritative sources.
- Resource and talent pressures: example given that the budget of the US Bureau of Labor Statistics has declined by an inflation-adjusted 22 percent since 2010, constraining competitiveness for talent, technology investment, and research.

### Roles and strengths of NSOs
- NSOs ground data in internationally harmonized concepts and methodologies and commit to transparency to build trust.
- NSOs provide benchmarks against which alternative and private data sources can be measured, helping ensure debates focus on policy rather than the validity of underlying numbers.
- Accurate, comparable statistics (GDP, inflation, unemployment) are essential for evidence-based policymaking in an interconnected global economy.

### Principles and policy recommendations
- Independence
  - Legal frameworks must protect NSOs from external interference, allowing them to choose methodologies and publish findings based on professional judgment.
  - Leaders should be empowered to make decisions grounded in statistical expertise; staff must adhere to high ethical standards and safeguard confidential data for statistical purposes only.
- Funding and capacity
  - NSOs need adequate funding to compete for talent, invest in technology, and conduct research on emerging data challenges (response rates, new survey techniques, innovative data sources) while continuing production of core statistics.
- Access and coordination
  - Governments should facilitate sharing of administrative data with NSOs.
  - Legal frameworks should enable secure and confidential data exchange between NSOs and other official statistics producers, such as central banks.
  - Best practice includes establishing national coordination committees to oversee statistical governance and promote collaboration.
- Innovation and data readiness
  - Integrate alternative data sources (administrative records, satellite imagery, private sector data) into statistical systems.
  - Make data AI-ready: well-structured metadata and access via application programming interfaces to enable discoverability and use by modern tools and platforms.
  - Cooperate with AI developers to ensure official statistical data are discoverable and accurately used by AI systems.

### Communication, dissemination, and public trust
- Invest in data visualization, interactive dashboards, and plain-language summaries to reach diverse audiences.
- Embrace open data initiatives and modern communication channels (social media and data portals) to engage the public and counter misinformation.
- Enhancing visibility and explaining NSO roles and data handling practices can improve survey response rates and public participation in data collection.

### Key statistics and exact figures cited
- mid-2000s (Argentina case timing)
- 2007 (year private analysts reported inflation up to triple official rate)
- triple (inflation up to triple the officially reported rate)
- 20 years (Nigeria delay before rebasing national accounts)
- 2010 (year Nigeria announced revised GDP after rebasing)
- almost 60 percent (size increase announced by Nigeria in 2010)
- below 15 percent (UK Labour Force Survey response rate in 2023)
- 2023 (year of the UK Labour Force Survey response rate drop)
- 22 percent (inflation-adjusted decline in US Bureau of Labor Statistics budget since 2010)
- 2010 (reference year for the budget decline)

*Instilling Trust through Statistics — Bert Kroese; F&D Magazine; December 2025 (Published on November 19, 2025).*

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_Source: https://www.imf.org/en/publications/fandd/issues/2025/12/point-of-view-instilling-trust-through-statistics-bert-kroese_