Socioeconomic Characterization: A Key Tool for Fair and Effective

In the design of public policy, one of the most critical—and often underestimated—elements is the socioeconomic characterization of the population. Accurately defining who the target population is forms the foundation of any effectiv, relevant, and equitable government intervention. Yet when this characterization is vague, arbitrary, or poorly measured, we risk reproducing inequalities and excluding those who most need protection.

What is Socioeconomic Characterization?

Socioeconomic characterization refers to the categorization of the population based on indicators such as income, education level, occupation, access to basic services, and housing conditions. While we often hear terms like “upper class,” “middle class,” or “lower-middle class,” the reality is that these categories can vary significantly across—and even within—countries.

Governments use this categorization to guide subsidies, design progressive tax systems, target social programs, and determine eligibility for scholarships, grants, and other public benefits. For that reason, how we define and measure these groups is not merely a technical issue—it is also a deeply political and ethical one.

When Characterization Fails: Costly Mistakes

Chile – The “Emerging” Middle Class and the 2019 Social Crisis

For years, the Chilean government used a targeting methodology that excluded millions of people from public benefits on the basis that they belonged to the "middle class." Yet in reality, this was a fragile middle class, lacking social protections or economic stability. In 2019, mass protests erupted across the country, exposing the deep disconnect between government statistics and people’s lived realities. The flawed socioeconomic categorization fueled social unrest and mistrust, revealing that the growth-centered model had failed to translate into equity or wellbeing.

India – Exclusion from Welfare Due to Identification Gaps

In India, access to food, healthcare, and education subsidies often depends on registration in the Aadhaar biometric ID system. While this system aims to improve efficiency, it has also excluded millions of poor and marginalized individuals who failed to register properly. Documented cases have shown people being denied food or medical assistance because their ID did not appear in the system—demonstrating that flawed or overly rigid categorization can literally become a matter of life and death.

When It's Done Right: Characterization That Drives Equity

Brazil – Bolsa Família and Multidimensional Targeting

One of the most successful examples globally is Brazil’s Bolsa Família program. It achieved significant reductions in extreme poverty and improved health and education outcomes. Its success was largely due to the Cadastro Único system, which collected detailed, multidimensional household data. Through home visits and contextual assessments, the state could accurately target support, minimizing both under- and over-inclusion.

Norway – Progressive Fiscal Policies and Universal Services

Norway employs a universalist model, but fine-tunes public services through progressive taxation and nuanced benefits. Instead of using rigid class categories, the state relies on intersecting indicators such as income, wealth, and individual needs, allowing redistribution to be effective and socially accepted—without stigmatizing beneficiaries or fostering resentment.

What Do International Standards Say?

From a human rights perspective, the principles of universality, non-discrimination, and progressivity must guide all public policy. This means that states have a duty to identify and prioritize those facing structural inequalities in the allocation of resources.

Organizations like ECLAC and UNDP encourage the use of broader, context-sensitive methodologies, while the Sustainable Development Goals (SDGs) stress the importance of “leaving no one behind.” To do so, countries must develop disaggregated, up-to-date, and evidence-based data systems.

Pathways Toward Fairer Characterization

• Include the voices of communities – People know their realities better than any survey can reveal. Including them in diagnostics helps prevent oversimplification and stigma.
• Continuously revise categories – Social and economic dynamics change over time. So should our classifications.
• Adopt multidimensional analysis – Beyond income, we must measure access to rights, social capital, job stability, and more.
• Use an intersectional approach – Women, Indigenous peoples, persons with disabilities, migrants, and other groups often face overlapping forms of exclusion that must be addressed.