Michele Coleman, UNCAC Coalition Gender, Inclusion & Corruption Working Group, Gender and Corruption Data Task Force Lead
Corruption is not a neutral force. It feeds on inequality, and it exploits the vulnerabilities created by gender. Yet too often, we continue to measure and fight corruption as if everyone experiences it in the same way. Traditional, gender-blind surveys and policies overlook how women and gender-diverse people are uniquely exposed. This is not only to bribes and favoritism, but also to coercive, non-monetary demands like sexual corruption. Simply put, women, men, and gender-diverse people encounter corruption differently. Women in particular are more likely to depend on public services like health care, education, and social protection, which are sectors more prone to bribery, favoritism, and abuse of power. Yet most publically available anti-corruption datasets and databases do not call attention to these stark impacts.
These blind spots matter. They mean abuses remain invisible, policies miss their mark, and power imbalances go unchallenged. For example, empirical studies suggest that higher participation of women in governance is associated with improved accountability and lower levels of corruption, especially in contexts with relatively strong institutions. But without gender-disaggregated data that supports learnings such as these and expanding to measuring corruption’s impact on the different genders, policymakers are effectively working blind. If we are serious about building accountable and equitable governance, we must confront this gap. We need a global push to standardize, expand, and disaggregate corruption datasets so that gendered realities are finally visible.
Our recent work in the UNCAC Coalition’s Gender and Corruption Data Taskforce set out to begin to understand this potential data gap by mapping what datasets and databases already exist that measure the intersection of gender and corruption, either directly or indirectly. Our non-technical evidence review identified 18 global, regional, and national datasets that touch on corruption and gender from an online literature search and from seeking stakeholder input. The databases and datasets needed to be publicly available or available upon request and recurring. This was not meant to be a scientifically systematic approach but instead as a comprehensive evidence review. From this, we developed a forthcoming public database to enhance data access for researchers, practitioners, and policymakers and will continuously add to this repository as we are notified of additional datasets. Moreover, we are promoting more rigorous and inclusive gender-disaggregated data collection, to support the development of gender-sensitive anti-corruption strategies that can address structural inequities and advance the rights of all genders.
From this evidence review and database creation, we found key insights and opportunities for data expansion and innovation. While there are significant corruption surveys that offer data disaggregated by gender, most rely on perceptions and self-reported experiences. Very few datasets capture gendered dimensions directly, sector-specific insights are limited, country-level specificity is weak despite global coverage, and qualitative insights are largely absent.
What we know
Perception dominates. Most datasets measure what people think about corruption or whether they report having experienced it. This perception data is often available by gender but is not typically analyzed or presented by gender. Further, few capture experienced-based or administrative data, such as the enforcement of anti-corruption laws or actual case records.
Consider the dominant approach of perception surveys. Tools like Afrobarometer in Africa or the Global Corruption Barometer surveys regionally ask citizens how corrupt they believe their leaders are, or whether they’ve been asked for a bribe. These are valuable snapshots, but they weren’t designed to capture how corruption operates differently for women and men, or for those whose gender identity already puts them at greater risk of exclusion. But it’s a starting point to guide us where to dig further.
Gender is an afterthought. Similar to the perception surveys, not every dataset we examined disaggregates or shares data by sex, which prevents analysis by gender. Corruption data remains dominated by quantitative surveys, with limited integration of qualitative evidence that could capture nuanced lived experiences by gender and context. Further, very few datasets directly address gendered corruption such as sexual corruption.
For example, if a woman is asked for sex in exchange for medical care, that experience may never show up in the statistics. If a girl is denied schooling because her family cannot pay an informal fee, her story is buried under broad averages. This invisibility isn’t accidental. It stems from data systems that were never designed to capture the gendered nature of corruption. At best, we get a cursory mention of whether men or women paid more bribes. At worst, gender vanishes altogether.
Regions are unevenly covered. Regional representation across datasets is uneven. Africa and Europe are comparatively better covered, with resources such as Afrobarometer, the Africa Integrity Indicators, the European Quality of Government Index, and various Global Corruption Barometer waves. In contrast, the Americas, Asia, and the MENA region largely rely on the Global Corruption Barometer and AmericasBarometer, leaving substantial gaps in regional and national-level coverage. Country-level datasets were even more limited, constraining efforts to design and target reforms based on evidence.
Sectoral blind spots are glaring. The areas where women are most exposed, such as healthcare, education, or other public service sectors, are precisely those where systematic gendered corruption data is missing. Overall, the evidence base is broad but shallow, fragmented across regions and sectors, and particularly weak in documenting gendered and sector-specific vulnerabilities, highlighting the need for systematic, gender-sensitive data collection going forward.
What can be done
Data is not just technical infrastructure; it is political. Choosing not to collect gendered corruption data or analyze by gender is a decision that keeps certain abuses hidden, and it has real consequences. Policies are designed based on what is measured. If gendered experiences remain invisible, they remain unaddressed.
In order to make corruption visible in all its gendered forms, we need to rethink how we collect, analyze, and share data. Here are seven priorities that emerged from our evidence review:
- Strengthen gender-disaggregated data collection. All corruption-related datasets should record and publish results by gender. This includes integrating gender indicators into existing surveys and governance indices.
- Ensure safe reporting mechanisms. Collecting sensitive data, especially on sexual corruption, requires survivor-centered safeguards to prevent retaliation or stigma.
- Promote mixed-methods research. Quantitative surveys need to be combined with qualitative interviews to reveal the lived experience behind the numbers.
- Invest in sector-specific research. Health, education, defense, and law enforcement are high-risk sectors for women, yet among the least studied.
- Standardize methodologies. Global alignment on definitions and indicators is needed to make gendered corruption data comparable across countries and over time.
- Raise awareness and political commitment. Campaigns and advocacy should highlight how corruption exacerbates gender inequality, mobilizing support for reforms.
- Use existing datasets more effectively. Many resources already exist but are not analyzed through a gender lens. Governments and researchers should capitalize on this strength.
The evidence is clear: corruption is not gender-neutral. It restricts rights, deepens inequality, and undermines trust in institutions. Yet our data systems often continue to treat corrupt practices as though it were a uniform phenomenon. This blind spot weakens both anti-corruption and gender equality efforts.. Until our corruption data reflects the realities of women and gender-diverse individuals, it cannot be meaningfully addressed.