The MENA Dilemma: Why Broad Categories Fail to Capture Complex Identities
Modern demographic systems often rely on broad categories to organize populations for research and policy, but these labels can obscure more than they reveal. One of the clearest examples is the category “Middle Eastern and North African” (MENA). By grouping Armenians, Persians, Turks, Arabs, Jews, Kurds, Amazigh peoples, Assyrians, and others under a single label, MENA flattens centuries of cultural, linguistic, and historical diversity into a single administrative box. Geography may justify including these populations together, but it does little to capture the distinct identities, traditions, and lived experiences that make each group unique.
The central tension is that MENA is fundamentally a geographic construct being used in contexts where people often expect ethnic, cultural, or even racial meaning. It describes a region, not a shared identity. As a result, it can bundle together populations whose languages, histories, and social structures differ dramatically. Turkish, Persian, Armenian, Arab, Kurdish, Jewish, Amazigh, and Assyrian communities all appear under the same umbrella in some classification systems, despite having distinct origins and internal diversity of their own.
Geographic logic explains why this happens. Countries such as Turkey, Iran, Armenia, Israel, and others are often grouped together because they fall within or adjacent to a broad “Middle East” region in global frameworks. But geographic proximity does not necessarily imply cultural or ethnic similarity. Within these borders exist multiple language families, religious traditions, and historical trajectories that do not map neatly onto a single category.
Supporters of MENA classifications argue that the category serves an important administrative and research function. In many systems, people from these regions were previously counted under broader or less precise labels, which made it difficult to study their specific health, economic, or social outcomes. A dedicated category can therefore improve visibility in data collection, allowing researchers to identify disparities that would otherwise be hidden.
However, the tradeoff is loss of resolution. When too many distinct groups are placed into a single category, the resulting data can become less meaningful for understanding lived experience. A regional label may be useful for broad comparisons, but it can obscure the differences that actually shape behavior, outcomes, and identity. This becomes especially important in fields like public health, education, and social policy, where fine-grained variation often matters more than broad regional similarity.
The issue is compounded when such categories are treated as proxies for culture or behavior. People within the same ancestry or geographic grouping may have vastly different lived experiences depending on migration history, neighborhood context, and social environment. For example, two individuals with similar ancestral backgrounds may have entirely different diets, health risks, and cultural norms if they were raised in different communities or countries. This means that ancestry or regional classification alone is often an incomplete predictor of real-world outcomes.
A more precise approach to demographic classification would separate different dimensions of identity instead of collapsing them into one. One layer could capture ancestry or ethnic background, allowing individuals to self-identify with multiple groups such as Armenian, Turkish, Arab, Kurdish, Persian, or others. Another layer could capture national or geographic origin, identifying connections to countries such as Turkey, Iran, Israel, or Armenia without assuming cultural uniformity within those borders.
In addition, it may be valuable in certain research contexts to capture cultural and social environment—essentially, the lived context in which a person was raised or currently lives. This includes factors such as immigrant community status, linguistic environment, urban or rural upbringing, and exposure to specific cultural norms. These variables often explain behavioral and health outcomes more directly than ancestry alone, yet they are rarely included in standard demographic datasets.
Finally, if race is still used for administrative or civil-rights purposes, it should remain a separate and optional category rather than being conflated with ethnicity or culture. Similarly, religion should be collected independently when relevant, since it cannot be reliably inferred from ancestry or nationality.
Ultimately, the limitations of the MENA category reflect a broader challenge in demographic classification: the tension between simplicity and accuracy. Broad categories make large-scale data collection easier, but they often sacrifice the nuance needed to understand real human diversity. A more layered system—one that distinguishes ancestry, geography, and lived cultural environment—would provide a clearer and more useful picture of populations while better respecting the complexity of identity itself.