In the spring of 1995, Venezuelan journalist Moisés Naím wrote that ‘[a]ny discussion of corruption is constrained by the impossibility of arriving at reliable data with which to measure corruption’s occurrence. Because corruption is, by design, covert, there is no real way of quantifying it...’ He was not alone in lamenting the field’s empirical limitations at the time, but later that same year Transparency International released the first edition of its Corruption Perceptions Index, marking the start of a quantitative revolution.

Today, corruption is measured in myriad ways, with experts monitoring, tracking, and benchmarking for a variety of purposes and with a constantly improving suite of tools. While there is more to be done, it is clear that we have come a long way since the days when corruption measurement was seen as an ‘impossibility’.

This 2024–2025 U4 blog series reflects that progress. Indeed, the contributors have explored a wide range of innovations, challenges, and opportunities in corruption measurement. They were selected as a cross-section of what the field has to offer, with a focus on cross-national initiatives but also some localised examples, and as a way to glean insights for the agenda going forward rather than for their collective comprehensiveness.

The evolution of corruption measurement

The measurement of corruption has driven and enabled the study of corruption more broadly. In the 1990s, the first generation of indices providing internationally comparable data supplied the empirical basis for testing long-standing assumptions and hypotheses. During the 2000s, comprehensiveness gave way to granularity, making policy targeting not only feasible but inevitable. Corruption was no longer purely covert, but a quantifiable phenomenon with identifiable, statistically recognisable victims and participants. In the 2010s, big-data approaches began to shed light on previously hidden manifestations of corruption, drawing in and engaging expertise from multiple disciplines. In the 2020s, the field has continued to experiment with new designs for producing corruption metrics.

Today’s measurement landscape offers a rich tapestry of approaches, data sources, and analytical methods that serve distinct objectives and complement – rather than supersede – one another. Among many other factors, large and small, we now track how people feel about their government’s record on corruption, how they experience these problems in routine interactions with public officials, the prevalence of illicit payments in business activities across the world, and deficits in organisational and national systems. We also track successes in preventing, detecting, and sanctioning wrongdoing.

Although the work is not complete, momentum is clearly building. The 2023 Vienna Principles Towards a Global Framework for the Measurement of Corruption – produced through a collaboration between UNODC, UNDP, OECD, and IACA (the International Anti-Corruption Academy) – reflect an emerging consensus regarding best practices for metrics design, data collection, processing, and use, and the importance of transparency and broad participation. As new tools proliferate, a community of researchers and practitioners dedicated to corruption measurement has begun to coalesce.

Emerging global frameworks and standards

In 2023, UNODC launched its Statistical Framework to measure corruption, moving towards a standardised global system with 153 indicators spanning experience, perception, risk, institutional frameworks, and enforcement. Its application will require augmented capacity and coordination of national authorities and should be linked to the UNCAC review mechanism.

By comparison, the Sustainable Development Goals, nearly a decade old, for the first time agreed on two indicators to measure corruption, with target 16.5 calling for ‘substantially reducing corruption and bribery in all their forms’. Despite being much less ambitious than the Statistical Framework, data collection for SDG 16.5 has highlighted some of the practical challenges associated with gathering this kind of information. However, it has also showcased some encouraging efforts to expand coverage and improve indicator quality.

In her blog post, Measuring progress on Sustainable Development Goal 16.5, Bonnie Palifka emphasises that current indicators and data collection (16.5.1 for bribe payment by individuals in the last twelve months, 16.5.2 for the same payment by businesses) lack clarity, frequency, and country coverage, making progress difficult to quantify.

Giulia Mugellini assessed 45 cross-national and national corruption surveys against a UN six-dimension quality framework (relevance, accuracy, reliability, periodicity, accessibility, comparability), and finds that only 50% provide data sufficient to monitor the SDG target. She recommends a modular design for surveys, balancing standardised core questions with local context.

Survey-based measures: Strengths and pitfalls

Robust measurements depend on clear definitions, well-crafted questions, and thoughtful design. Citizen survey responses on corruption can be misleading: citizens may respond even when unsure, interpret ‘corruption’ in different ways, and be influenced by social desirability or political bias.

The DATACORR database of over 3,000 survey questions enables practitioners to design more nuanced surveys – distinguishing generic vs specific or personal vs societal corruption experiences – to improve measurement quality. Crucially, disaggregation by specific demographics allows researchers to identify, for example, how men and women may experience corruption differently, uncover hidden patterns and vulnerabilities, and tailor interventions accordingly – a key step toward inclusive and effective anti-corruption policy.

Innovative approaches and indices

Our blog series showcased innovations in measuring corruption more precisely, dynamically, and usefully – moving beyond blunt, perception-based indices towards richer, multi-dimensional, and context-sensitive tools.

The Unbundled Corruption Index (UCI) breaks corruption into four types (petty theft, grand theft, speed money, and access money), revealing how each of them differ across regimes. The Corruption Risk Forecast (CRF) uses the Index for Public Integrity over time to project real changes in trends instead of static reputational snapshots. The OECD Public Integrity Indicators provide criteria-based measures (eg conflict of interest or audit systems), filling data gaps and exposing implementation weaknesses.

Other blogs apply these measurement approaches to specific domains. The Global Public Procurement Dataset tracks corruption red flags in public contracts in 42 countries. The CO.R.E. project adapts red-flag models for emergencies, combining crisis-specific indicators with machine learning to distinguish anomalies from legitimate emergency adaptations. A sectoral study in the Czech Republic taps insider perspectives across six high-risk sectors (health, education, sport, construction, procurement, and debt collection) to uncover everyday corrupt practices that broad indices often miss.

The UK Enforcement Tracker visualises hard-to-access data on domestic enforcement, while the Financial Secrecy Index exposes jurisdictions that enable illicit financial flows. Both translate complex datasets into accessible evidence, reveal systemic weaknesses, and confront data gaps and definitional challenges.

Lessons for the way forward

Our 15-piece series reveals an exciting shift: corruption measurement is moving from abstract perceptions to concrete, actionable metrics. The approaches we have featured represent the state of the art in corruption measurement, building on earlier tools and pushing innovation forward one step at a time.

As the conversation expands and deepens, it yields a methodological jigsaw that helps stakeholders gain a more nuanced picture of corruption in their context – whether a country, industry, city, or organisation. Each new piece reveals more of the rich tapestry that corruption measurement represents. From that nuance, better-calibrated policy instruments emerge, underscoring that the future of corruption measurement lies in bespoke indicators and greater data granularity.

In practice, this means mixing and matching approaches as cases require and, wherever possible, triangulating data. No single framework, data source, or method can overcome all the challenges posed by attempting to measure a partially hidden set of practices. That limitation should be treated as a design feature, not a flaw. For every critique of perception measures’ subjectivity, experience-based surveys’ narrow scope, or integrity metrics’ role as proxies, there is a corresponding recognition that each contributes unique evidence – useful for some measurement goals even if not for all – and remains valuable in its own right.

At the end of the day, what matters is disciplined alignment between producers and users: in other words, clear communication about what a tool measures, how, and for whom. Anchoring that dialogue in shared principles – such as those outlined in the Vienna Principles – can sustain methodological rigour, improve comparability, and keep results policy-relevant. Improving corruption measurement is not just a technical endeavour, but a step towards stronger accountability and governance.