A Comparative Investigation: Cameroon and Mali
YAOUNDÉ, Cameroon — In early 2024, Cameroon’s National Institute of Statistics released something that should never have taken this long: the country’s first Gender Factbook. A 200-page compilation, it gathered comprehensive statistics on women’s lives in Cameroon. Its arrival exposed a deeper problem shaping millions of African women: policies, protections, and development plans are built on incomplete data or on no data at all.
UN Women’s 2020 assessment found that Cameroon had only 48.4% of the gender-relevant SDG indicators available. Mali had 45.9%. When governments cannot count women, they cannot see violence, measure discrimination, allocate resources, or demand accountability. The cost of not counting her is measured in lives lost, violence hidden, and rights denied.
The Numbers Behind the Invisibility
The Gender Factbook confirmed what Cameroonian women have long known: their labor, their risks, and their realities were invisible to policy makers. Women perform 8.2 more hours of unpaid care work per week than men, over 426 hours a year that fuel households and communities but are absent from GDP, pensions, and policy.
Violence statistics are stark. 56.4% of Cameroonian women have experienced at least one form of violence. In Yaoundé the rate is 64%, nearly two in three women. Among women in union, 43.2% face domestic violence. These figures existed in DHS surveys. What changed in 2024 is visibility. Cameroon brought the data together and made it usable for policy.
Mali’s situation is worse. Most gender data still comes from 2018, including the finding that 18.4% of women faced intimate partner violence in the previous year. Conflict has since made comprehensive data collection nearly impossible. When data gaps widen, violence becomes easier to deny and harder to stop.
When Violence Goes Uncounted
Data gaps trigger predictable harms.
- Violence stays hidden. No baseline means no way to measure change.
- Resources get misallocated. Yaoundé, with 64% prevalence, risks receiving the same resources as lower-risk regions.
- Vulnerabilities remain unseen. Education, age, income, disability, rural residence shape risks. Without disaggregation, policy is blind.
- Accountability disappears. Governments cannot be held responsible for indicators they fail to measure.
The Child Marriage Data Desert
Mali’s child marriage statistics illuminate how dangerous data gaps become when combined with harmful practices.
The 2018 survey showed that 53.7% of women aged 20–24 were married before age 18. That’s more than half of young women robbed of childhood, denied education, exposed to early pregnancy risks, and trapped in adult responsibilities before their brains have fully developed. But that 53.7% figure is now seven years old. Has the rate improved? Worsened? Remained stable? Which regions have the highest rates? Which communities have successfully reduced child marriage? What interventions work?
Mali’s government can’t answer these questions with recent data. The 2018 Demographic and Health Survey remains the most recent comprehensive source. Subsequent conflict has prevented follow-up surveys. International organizations that might conduct alternative data collection face access challenges in conflict-affected regions.
The result is policy paralysis. Mali has laws prohibiting marriage before age 18. The country has ratified international conventions protecting children from forced marriage. NGOs run awareness campaigns. Yet without current data, no one knows if any of it works. Resources continue flowing to programs that might be ineffective, while potentially successful interventions go unscaled because there’s no evidence of their impact.
Cameroon faces similar, though less severe, data gaps on child marriage and other harmful practices. The 2018 DHS provided baseline data, but continuous monitoring remains limited. Anecdotal reports suggest certain regions have higher rates than others, but comprehensive, recent, disaggregated data to guide targeted interventions doesn’t exist.
This is not just a data gap. It is a protection gap.
The Progress Paradox: Why Cameroon’s Numbers Look Worse
Between 2022 and 2024 Cameroon’s gender data availability rose from 57% to 75% thanks to UN Women’s Women Count program. The Factbook is the result of that investment.
Better data made old problems newly visible. The rise in reported violence rates does not mean violence increased. It means Cameroon can now see what was already there.
This creates a paradox. The more you count, the worse your numbers look at first. That can create political hesitation. Cameroon chose visibility. Mali, hindered by insecurity, could not.
The Time Tax: Counting Invisible Labor
Unpaid care work remains one of the most powerful and least counted barriers to women’s economic participation.
Cameroonian women’s extra 8.2 hours weekly equal a full-time burden over a year. In Mali the imbalance is larger: 22.1% of women’s time is unpaid care work versus 1.7% for men.
Without regular time-use surveys policymakers cannot tell whether childcare programs or leave reforms reduce the burden. Time poverty remains invisible and rarely funded.
Political Representation: The One Indicator Both Countries Count Precisely
One gender indicator both countries track well is women’s presence in parliament.
- Cameroon: 33.9%
- Mali: 28.6%
- Global average: 26.9%
Counting parliamentarians is simple. Counting violence, unpaid labor, or maternal deaths is harder. What gets counted reveals what gets valued.
When Conflict Erases Women From Data
Mali shows how quickly statistical systems collapse in crisis. Survey teams cannot safely reach conflict zones. Displaced women are erased from population data. Death registrations fail. Maternal mortality becomes untraceable.
Mali may know less about women’s conditions now than in 2018. Yet policies continue without accurate evidence.
Why Counting Women Is Worth the Investment
Gender data systems cost money, but ignorance is costlier.
Better data enables targeted service delivery, efficient allocation, measurable progress, and evidence for reform. In Cameroon knowing Yaoundé’s violence burden is 64% helps direct resources. In Mali the absence of data means interventions are blind.
From Data to Action
Data alone does not change outcomes. Action does.
For Cameroon the Gender Factbook should be a baseline updated yearly, a policy guide, and a tool for accountability. For Mali the priority is restoring basic statistical capacity and integrating gender into humanitarian data systems.
Africa’s Gender Data Crisis
Across Africa many countries lack recent violence surveys. Time-use surveys are rare. COVID-19 reversed progress. Funding shortages cripple national statistics offices.
Demand for gender data is rising. Investment is not. Cameroon shows what is possible. Mali shows why the work is urgent and fragile.
Recommendations — What Must Change to Truly Count Women
1. Make gender data a permanent national priority, not a project.
Cameroon’s progress happened because institutions were funded, trained, and mandated to produce gender-disaggregated statistics. Every African country should establish dedicated gender statistics units within National Statistics Offices with recurring budgets—not donor-dependent pilots.
2. Measure violence routinely, safely, and nationally.
Violence against women must be tracked at least every five years using internationally recognized methodologies. Mali shows what happens when conflict halts data collection: women’s suffering disappears. Governments must protect survey teams and integrate GBV modules into all major national surveys.
3. Disaggregate everything. Absolutely everything.
Administrative data—births, deaths, school records, police reports, court dockets, voter rolls, employment data—should all be sex-disaggregated by default. This is low-cost, high-impact, and immediately exposes where women’s rights are breaking down.
4. Count unpaid care work as a national economic priority.
Time-use surveys should be conducted at least once per decade, with lighter interim surveys in between. Without measuring women’s unpaid work burden, policies on women’s employment, childcare, or parental leave cannot be evidence-based.
5. Build data systems that work during crises, not only in stability.
Mali’s collapse shows that gender data disappears fastest in conflict, precisely when women need protection the most. Governments and humanitarian actors must integrate gender-sensitive data collection into all emergency responses, including displacement tracking, healthcare assessments, and food security surveys.
6. Create public, accessible platforms where data is freely available.
Data locked in a PDF is as good as invisible. Countries should adopt open-data portals, like Cameroon’s 2024 Factbook model where researchers, journalists, civil society, and policymakers can easily access gender statistics in usable formats.
7. Incentivize accountability by linking funding to gender data.
Regional bodies, donors, and governments should tie part of development financing to improvements in gender data systems. This shifts gender statistics from a “nice to have” to an institutional requirement.
8. Center women in the process—not just in the numbers.
Women’s rights groups, statisticians, advocates, and survivors must be included in designing surveys, deciding indicators, and interpreting results. Data systems built without women are guaranteed to misrepresent women.
Conclusion: The Price of Invisibility
In Cameroon data has made women visible. In Mali many women remain erased. The difference is not destiny. It is data. You cannot manage what you cannot measure. You cannot protect who you do not count.
Cameroon’s progress shows change is possible. Mali shows why it is urgent. The cost of not counting her is too high.
Author’s Note
This WanaData story was supported by Code for Africa and the Digital Democracy Initiative as part of the Digitalise Youth Project, funded by the European Partnership for Democracy (EPD).
