Propcom+ monitoring data from the beginning of Year 2 revealed that less than 25% of farmers receiving chicken vaccinations were female. At first glance, this might not seem alarming, considering the programme operates in northern Nigeria—a region known for stark gender inequalities. However, a more detailed look told a different story. We know the poultry sector is overwhelmingly female-dominated in Nigeria. Women primarily own chickens, while men tend to own larger, more profitable livestock like cattle (World Bank, 2022). So, how were we missing so many women in our data?
The Hidden Flaw in Data Collection
A closer examination of the vaccination and data collection process revealed the issue. Typically, male family members take chickens to be vaccinated. The reason is simple. The workforce of vaccinators, such as Community Animal Health Workers, is largely male and cultural and social norms in northern Nigeria restrict male-female contact outside the family. So, even when a woman owns the chicken, her husband, father, brother, or another male relative would take the chicken to the vaccination site. As a result, the male family member was recorded as the beneficiary, rather than the woman who owns and cares for the chickens.
This method of data collection has led to a significant oversight. Instead of capturing the women who are the primary poultry farmers and who benefit from the improved health of their animals, we were documenting the wrong beneficiaries. Our data, as a result, underreported the impact on women and failed to account for the true role of women in the poultry sector.
A Broader Issue in Data Quality
This is not a single isolated case of data collection gone wrong; it points to a more systemic challenge of measuring inclusive impact in Market System Development (MSD) programming. For Propcom+, like many MSD programmes, monitoring data is gathered through implementing partners. Animal health workers record the number of male and female beneficiaries reached through vaccinations or agro-dealers are asked to report on the disability status of their customers. In effect, these partners take on the role of researchers, collecting crucial monitoring data that can be influenced by complex social dynamics and biases, especially in areas like gender and disability.
When data collection occurs in a context shaped by household power imbalances or deeply ingrained social norms, it can lead to significant distortions in reporting. This undermines the programme’s ability to accurately measure impact—especially for excluded groups like women, people with disabilities and internally displaced people.
What’s the solution?
The first step we took was to critically assess the data we collected. Uncovering inaccuracies, as seen in the poultry vaccination data, allowed us to identify where the process was failing. In addition to recognising the problem, we adjusted the data collection by adding a simple question to clarify livestock ownership. Additionally, with Propcom+ support, partners have conducted refresher trainings with Community Animal Health Workers, emphasising the importance of accurately determining ownership.
Adjusting data collection methods paid off, as reflected by Propcom+ monitoring data. Each quarter saw an increase in women among new beneficiaries (see Table 1), with the most recent quarter recording almost 60% female beneficiaries. In total, in Year 2, the programme’s animal health interventions reached 45% women farmers.
Table 1: New animal health beneficiaries (sex-disaggregated)
Source: Propcom+ monitoring data, March 2025
Beyond simply adjusting data collection tools, it is necessary to rethink how we collect data on Gender Equality and Social Inclusion (GESI). Relying solely on quantitative data does not capture the full picture, especially when it comes to complex, socially embedded issues like ownership and caring roles. Complementing quantitative data with qualitative insights will provide a more nuanced understanding of who truly benefits from Propcom+ interventions. This cannot be left in the hands of partners alone, but is a core programme responsibility to be captured through baselines, endlines, impact evaluations and targeted GESI studies, such as the Gender Deep Dive we’ve completed on the Systemic Rice Intensification (SRI) pilot or the barrier analyses we routinely undertake to inform intervention design.
Looking ahead, building the capacity of our partners to collect high-quality data will be critical. This involves not just training them to gather information but also ensuring they understand the importance of capturing the realities of vulnerable groups. By doing so, we can begin to address the systemic biases in our data collection processes and, ultimately, provide a more accurate measure of programme impact on women and other underrepresented groups. At the same time, partners gain valuable insights into reaching underserved consumer groups, ultimately boosting revenue. Our GESI Partnership Strategy involves supporting partners to target women and improve impact measurement. While this requires effort and will not happen overnight, data can be an entry point to support partners to shift attitudes and question their own bias. Ultimately, this increases the potential for a more inclusive programme impact.
