R-P05 · TOPIC · institutional-trustGRF dossier · service level R0
GRFInformation Integrity and Public TrustIn development
Topic dossier · Information Integrity and Public Trust

Institutional trust

Trust in governments, courts, science and media lets institutions act with consent and lets people rely on information they cannot check themselves. Trust rises and falls with performance, fairness, openness and the experience of being heard. This page sets out how institutional trust is built and lost, the surveys and records that measure it, and the institutions whose decisions shape it.

01In brief

The short answer.

Institutional trust is the confidence people place in governments, courts, science and media to act competently and fairly. It rises and falls with performance, fairness, openness and voice, and increasingly divides along political lines. Heads of institutions, legislatures and media organizations shape it through their decisions.

02Geography

People and hazards in Eastern Europe.

For institutional trust, in GRF's situational-awareness reading, the map opens on population density and night lights with built-up and settlement extent from NASA GIBS, framed on Eastern Europe; 4 further NASA layers wait in the list.

The interactive map needs JavaScript; the layer list and sources are below.

Layers 2

People and hazards, for this page

Latest available

NASA GIBS / EOSDIS · ASTER GDEM (METI, NASA) · Blue Marble (NASA) · © OpenStreetMap contributors (ODbL) · Natural Earth

Loading the latest NASA imagery.

Drag to move. Ctrl or ⌘ and scroll, pinch or the buttons to zoom; hover for the values under the pointer; reset returns to the region.

For institutional trust, in GRF's situational-awareness reading, the map opens on population density and night lights with built-up and settlement extent from NASA GIBS, framed on Eastern Europe; 4 further NASA layers wait in the list.Satellite and model products as NASA publishes them, each with its own date; indicators, not assessments of any place; claim ceiling INTEGRATED.

Data: NASA GIBS · ASTER GDEM · Blue Marble · © OpenStreetMap contributors · Natural Earth · GDACS · CIESIN/Columbia, NASA SEDAC HBASE · NASA EONETSources and licences

  • We acknowledge the use of imagery provided by services from NASA's Global Imagery Browse Services (GIBS), part of NASA's Earth Science Data and Information System (ESDIS). Colours redrawn from the published colour map; values averaged into H3 cells where shown (modified). NASA open data
  • We acknowledge the use of imagery provided by services from NASA's Global Imagery Browse Services (GIBS), part of NASA's Earth Science Data and Information System (ESDIS). Colours redrawn from the published colour map; values averaged into H3 cells where shown (modified). NASA open data
  • Center for International Earth Science Information Network (CIESIN), Columbia University. 2018. Gridded Population of the World, Version 4 (GPWv4): Population Density, Revision 11. NASA Socioeconomic Data and Applications Center (SEDAC). Licensed under CC BY 4.0. Colours redrawn from the published colour map; values averaged into H3 cells where shown (modified). CC BY 4.0
  • VIIRS Black Marble night-time lights, NASA Goddard Space Flight Center; distributed by the NASA Level-1 and Atmosphere Archive and Distribution System (LAADS DAAC); imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into H3 cells where shown (modified). NASA open data
  • VIIRS near-real-time products (flood, land surface temperature, snow cover, corrected reflectance), NASA LANCE; imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into H3 cells where shown (modified). NASA open data
  • NASA Fire Information for Resource Management System (FIRMS), VIIRS 375 m active fire product, NASA LANCE; vector tiles via NASA GIBS. Colours redrawn from the published colour map; values averaged into H3 cells where shown (modified). NASA open data
  • ASTER GDEM is a product of METI and NASA (ASTER Global Digital Elevation Model V003, NASA LP DAAC); imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into H3 cells where shown (modified). NASA open data
  • Made with Natural Earth. Free vector and raster map data @ naturalearthdata.com. Public domain
  • © OpenStreetMap contributors (ODbL), via NASA GIBS (coastlines, boundaries, roads and place names). Colours redrawn from the published colour map; values averaged into H3 cells where shown (modified). ODbL
  • Blue Marble, NASA Earth Observatory; imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into H3 cells where shown (modified). NASA open data
  • Context on the globe and flat map: coastlines, boundaries, roads and place names from NASA GIBS reference layers (OpenStreetMap data, ODbL, © OpenStreetMap contributors); Blue Marble imagery as an optional base; 3D terrain and the elevation read-out from the ASTER GDEM elevation colour index (5 m to 200 m classes, exaggerated for legibility). Boundaries are drawn as published and imply no position on any territory.
  • Globe: MapLibre GL JS 6.9.1, BSD-3-Clause, © MapLibre contributors.
  • Live: GDACS (EC JRC / UN OCHA) Events are indicative, not official warnings.
  • Context: CIESIN/Columbia, NASA SEDAC HBASE. Center for International Earth Science Information Network (CIESIN), Columbia University, and NASA Socioeconomic Data and Applications Center (SEDAC): Global Human Built-up And Settlement Extent (HBASE) 2010; Seto, Guneralp and Hutyra 2012, Global Grid of Probabilities of Urban Expansion to 2030; Global Urban Heat Island (UHI) Data Set, 2013.
  • Live: NASA EONET Events are indicative, not official warnings.
  • Optional layers (5 sources in the layer list: live, forecast, observed and context) print their provider credit on the map and here when switched on; forecasts are model output and derived indices are estimates.

Layers and dates

  • Population density, 2020: Population: CIESIN, Columbia University, Gridded Population of the World v4.11 (2018), NASA SEDAC, CC BY 4.0 · static
  • Night-time lights, daily: VIIRS Black Marble (VNP46A2), NASA LAADS DAAC · 12 Oct 2026
  • Flood water, 3-day composite: VIIRS Global Flood Product, NASA LANCE · 14 Oct 2026
  • Active fires and thermal anomalies, last day: VIIRS NOAA-20 375 m (FIRMS), NASA LANCE FIRMS · 13 Oct 2026
  • True colour: VIIRS NOAA-20 corrected reflectance, NASA LANCE · 13 Oct 2026
  • Snow cover: VIIRS NOAA-20 NDSI, NASA LANCE · 13 Oct 2026

03Console

Narrative monitor, in outline.

The working screen behind this subject: its structure, terms and states. Names are placeholders and values illustrative.

Narrative monitor

Institutional trust · Crisis information cell · Week 24

Harmful rumour spreadingGRF · Information Integrity and Public Trust

Narratives

Tap water poisoned after flood92HighFalseCorrection out
Shelters charging fees31MediumFalseDrafting
Dam about to fail41HighCheckingHolding line
Aid only for registered voters43MediumFalsePrebunked

Spread

Response protocol

  1. Detectmonitor
  2. Verifyprovenance
  3. Assess harmthreshold
  4. Respondcorrect · prebunk
  5. Reporttransparency
Narrative monitor: the screen's structure, terms and states for Institutional trust.Placeholder names and illustrative values; a design, not a record or a live system.
04The question

Institutional trust: how the risk develops.

Evidence, transparency and public confidence. The dossier below sets out how the risk develops, what is measured, where the data come from, how it is modelled and who decides.

Mechanism

Performance and fairness

People judge institutions on whether they deliver and whether they treat people fairly. Experiences of unfair treatment reduce trust more than general dissatisfaction with outcomes.

Mechanism

Openness signals integrity

Publishing decisions, data and reasons lets people see how institutions work. Secrecy and unexplained reversals invite suspicion even when decisions are sound.

Mechanism

Polarisation splits trust

Trust increasingly follows political identity. An institution trusted by one group can be distrusted by another, which makes any message from it divisive.

Mechanism

Lost trust is slow to rebuild

Scandals and failures cut trust quickly; recovery takes sustained change. Corrections and accountability help only when they are visible and credible.

05Key terms

Terms used on this page.

Each term as this page uses it. Where a standard-setting body defines the term, the wording follows that body.

TermDefinition
Warranted trustTrust that matches an institution's actual reliability, as distinct from blind trust or unjustified distrust.
Procedural fairnessThe perception that decisions are made through fair, transparent and respectful processes.
Affective polarisationDislike and distrust of people and institutions associated with opposing political groups.
06Indicators

What is measured, and how well.

Each indicator carries its unit, its cadence, the kind of source behind it and the evidence label a value would carry. No values are shown: a programme supplies them under its own authority.

IndicatorUnitCadenceSource typeLabel
Trust in national governmentshare of respondents with high or moderately high trustbiennialOECD Trust SurveyESTIMATED
Confidence in the judiciaryshare of respondentsperiodicWorld Values Survey and Gallup World PollESTIMATED
Trust in scientistsshare of respondentsperiodicWellcome Global MonitorESTIMATED
Trust in newsshare of respondents who trust most news most of the timeannualDigital News ReportESTIMATED
Perceived fairness of public servicesshare of respondentsbiennialOECD Trust SurveyESTIMATED
Open government data availabilityscorebiennialOECD OURdata IndexESTIMATED
07Canvas

From sources to decisions.

The topic as a computational graph: named sources feed the methods, an evidence review gates the outputs, and the decisions stay with the authorities that hold them.

Pipeline canvas · Institutional trust as a run graphDesign, not a record
Institutional trust: responding to harmful informationPublic narratives are monitored; items are detected and their provenance verified; harm is assessed against a threshold; the response (correct, prebunk, label, escalate) is chosen and reported transparently. Design of the process, not a record of a run.

Fig. 02Nodes carry their type, two metric rows and a state; connectors show what feeds what; dashed frames name the owners; the timeline is the cadence. Design of the pipeline, not a record of a run.

08Sources

Where the evidence comes from.

Named public datasets and institutional records, each with its licence class. Sources by agreement are used only under their terms and never republished.

SourceWhat it givesLicence class
OECD Trust SurveyTrust in public institutions and its drivers across member countriesFree, OECD terms
World Values SurveyValues and confidence in institutions across countries and wavesFree for non-commercial use, terms to check
Wellcome Global MonitorTrust in science and healthFree to read, terms to check
Reuters Institute Digital News ReportTrust in news and outletsFree to read, terms to check
V-Dem datasetIndicators of democracy, media freedom and accountabilityCC BY-SA 4.0
09Decisions

Who decides, and what the evidence serves.

Information Integrity and Public Trust prepares evidence. The decisions below are taken by the authorities named, under their own rules, and never by the platform.

Who decidesThe questionEvidence this page serves
Heads of public institutionsHow to publish decisions, data and reasonsEvidence on drivers of trust and comparable practice
Public service leadersHow to improve fairness in service deliverySurvey evidence on fairness and trust
LegislaturesWhat transparency and accountability duties apply to institutionsComparative open government frameworks
Media organizationsHow to show their methods and correctionsTrust research and professional standards

Outputs this topic produces

  • Guide to trust surveys and what their questions measure
  • Comparative notes on drivers of institutional trust
  • Dialogue briefs on transparency and corrections as trust practices
  • Questions for public institutions on trust-building
10Core questions

What experts ask.

Trust researchers ask what makes institutions trustworthy and how people judge them.

  • What drives trust in public institutions: competence, integrity, fairness, shared values or results?
  • Why does trust differ between groups, and how do experiences of unfair treatment affect it?
  • How does transparency affect trust, and when can it backfire?
  • How quickly is trust lost after failures, and how is it rebuilt?
  • How should institutions respond to justified distrust?
11Mechanisms and drivers

Trustworthiness, not only trust.

Research distinguishes trust, a belief held by the public, from trustworthiness, a property of institutions. People tend to judge institutions on competence, integrity and benevolence, and on whether they are treated fairly. Personal and community experience, such as encounters with police, health services or welfare offices, strongly shapes trust, and historic mistreatment of particular groups can create well-founded distrust.

Transparency can build trust when it reveals competent, fair processes and honest acknowledgement of uncertainty. It can reduce trust when it exposes failures without accountability. Trust lost through scandal or broken promises recovers slowly. The aim for institutions is not to maximise trust uncritically but to deserve it and to make that visible.

12Methods and data

How the evidence is built, and where it stops.

  1. 01

    Trust surveys

    Measuring trust over time and across groups with social and behavioural science. Survey questions capture general attitudes, not specific judgements.

  2. 02

    Experimental studies

    Testing how transparency or communication affect trust. Effects in experiments may not persist.

  3. 03

    Comparative institutional analysis

    Linking institutional features to trust across countries. Causation is hard to establish.

  4. 04

    Qualitative research

    Understanding how people experience institutions. Findings are rich but not generalisable alone.

13Methods

How it is studied, and what the methods cannot say.

  1. Social and behavioural scienceMeasuring trust and exposure · Testing interventions · Qualitative study of information needsApplied here
  2. Network science and contagionDetecting coordinated behaviour · Tracing narrative spread · Mapping media ecosystemsApplied here
  3. Artificial intelligence and machine learningClassifier error in moderation · Synthetic media detection limits · Recommender auditsApplied here
14Questions

Questions and answers.

Is high trust always good?
Not necessarily. Researchers distinguish warranted from unwarranted trust; healthy systems pair trust with the ability to check and hold institutions to account.
What drives trust in government?
OECD research links trust to reliability, responsiveness, openness, integrity and fairness, and to people's sense that they have a say.
Why does trust differ between groups?
Experiences of institutions, political identity, education and exposure to different information sources all shape trust.
Can communication campaigns build trust?
Communication alone has limited effect. Trust follows experience of competent and fair institutions, which communication can make visible but not replace.
Does GRF measure trust in specific institutions?
It is designed to publish research and comparative cases. It does not rate or rank individual institutions.
Information Integrity and Public Trust

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