Inclusion is not a matter of political correctness. It is the key to growth.
— Jesse Jackson
We live in a world where data drives decisions. From digital dashboards to policy reports, evidence has become the foundation of modern governance. Evidence undoubtedly provides a strong foundation for informed decision-making. But evidence by itself is not enough. Numbers tell us what is happening but they rarely explain why it is happening or who is being left behind.
For public policy to create meaningful and lasting change, evidence must be interpreted through the lens of gender, equity, and lived realities. Otherwise, even well-intentioned policies risk reinforcing the very inequalities they seek to address.
Beyond the Numbers - What Data Doesn't Tell Us
To understand why, consider a real-life example. Imagine looking at a dataset that shows an unusually high number of hysterectomies among women in a single district. The numbers point to a health crisis. But they don't tell you why. That was the reality in Maharashtra's Beed district, where investigations uncovered a disturbing pattern among women employed as seasonal sugarcane cutters. Many underwent hysterectomies at a young age not because they always medically required the procedure, but because missing work due to menstruation, pregnancy, or reproductive health issues could mean losing wages in an exploitative labour system. For many women, the choice was not between surgery and good health; it was between surgery and economic survival.
If policymakers had relied only on hospital records, they would have identified an increase in surgeries and perhaps tightened regulations on private healthcare providers. While necessary, that response alone would have missed the larger picture. The real issue extended far beyond healthcare. It lay at the intersection of labour rights, migration, poverty, women's health, and deeply entrenched gender inequalities.
When Evidence Misses the Bigger Picture
Evidence is often treated as objective and value-neutral. Yet the questions we ask, the data we collect, and the indicators we choose to measure determine what becomes visible and what remains hidden. National averages can mask regional disparities, aggregate statistics can overlook marginalised communities, and quantitative data alone rarely captures the influence of social norms, power structures, or discrimination.
This is where inclusive policymaking makes the difference. It broadens the evidence base by combining data with lived experiences, community voices, and qualitative insights, ensuring that policies address not only immediate outcomes but also the structural barriers that shape them.
The Invisible Work Behind the Gender Gap
India's labour market illustrates this challenge. Despite economic growth, women's labour force participation remains significantly lower than men's. Looking only at employment statistics could suggest that women are simply choosing not to work. However, India's Time Use Survey reveals that women spend nearly seven times more time than men on unpaid domestic and caregiving responsibilities. This invisible work limits their ability to pursue paid employment, yet it remains largely absent from conventional economic indicators. The implication is clear: creating jobs alone will not increase women's workforce participation. Policies must also invest in childcare, safer public transport, flexible work arrangements, and the recognition of unpaid care work. Evidence became meaningful only when employment data was understood alongside women's everyday realities.
What Inclusive Policymaking Looks Like
Inclusive policymaking is not about collecting more data; it is about ensuring that evidence reflects the realities of those who are often left behind. India's Aspirational Districts Programme (ADP) demonstrates this approach. Instead of relying on national averages, it identified the country's most underdeveloped districts using data on health, education, nutrition, and financial inclusion. By combining real-time monitoring with local governance and context-specific interventions, the programme enabled solutions tailored to each district's unique challenges.
The lesson is simple: evidence is most powerful when it is paired with local realities, ensuring that policies reach the communities that need them the most.
From Evidence to Lasting Change
The future of policymaking will not be defined by how much data governments collect, but by how well they understand the people behind that data. Evidence can identify trends and measure progress, but lasting change requires recognising the unequal realities that shape people's opportunities and choices.
Inclusive policymaking asks better questions, listens to diverse voices, and ensures that those who have historically remained invisible are part of both the evidence and the solution. When research is combined with empathy, participation, and equity, policies move beyond being efficient and thus become transformative.
