What happens when regional boundaries change — and why it matters for development?

Afghanistan and Pakistan’s recent move from South Asia (SA) to Middle East and North Africa — now reflected in the World Bank’s regional grouping known as Middle East and North Africa, Afghanistan and Pakistan (MENAAP) — may seem like a procedural update, yet it subtly reshapes how development is analyzed and interpreted. 

Regional classifications do more than organize maps: they structure programs, guide analytical work, and shape the narratives used to assess progress. When countries move between regions, the statistical story of those regions moves with them, altering how researchers and practitioners interpret regional aggregates.

Because the World Bank’s 189 member countries are organized into regions aligned with operational and analytical priorities (for an overview, see Figure 1), any reclassification changes how regional aggregates are constructed and how trends are interpreted over time. These groupings reflect more than geography — they capture shared development challenges, economic structures, and cross-border linkages that explain why certain economies are analyzed together. When regions are redefined, the context for interpreting trends shifts as well. 

Regional boundaries matter for four reasons: they shape analytical comparability by grouping economies with similar structures or constraints; support operational coordination across programs and regional initiatives; determine the statistical composition of aggregate indicators used to track development; and guide how broader regional development patterns are interpreted over time. These effects become especially clear when comparing Pre-MENAAP with MENAAP or Pre-SA with SA.

How do regional reclassifications change the data we see — and what do the shifts reveal?

Population and age

Changes in regional composition alter population totals considerably. For 2024, the population rises from 519 million in Pre-MENAAP to 813 million in MENAAP, with Pakistan representing the largest country in in the new regional group at nearly one-third of the total. 

In contrast, the population decreases from about 2.0 billion in Pre-SA to 1.7 billion in SA, yet the effect on population shares is less pronounced: while the share of India increases from 75% in Pre-SA to 86% in SA, the shares of other countries — such as Bangladesh, Nepal, Sri Lanka, Bhutan, and the Maldives — rise slightly. 

The changes in population affect, likewise, indicators referring to population, such as the age-dependency ratio³. The age structure in MENAAP becomes noticeably younger relative to Pre-MENAAP, as the young-age dependency ratio⁴ rises by approximately 15%, while the old-age dependency ratio⁵ falls by roughly 7%. Conversely, the young-age dependency ratio declines slightly from 40% in Pre-SA to 37% in SA, while the old-age dependency ratio rises marginally, and the total dependency ratio⁶ falls modestly by comparing Pre-SA to SA.

Economy and growth

The revision of the regional composition also affects economic indicators. For 2024, the total GDP (current US$) of MENAAP is 8.5% higher relative to Pre-MENAAP, while SA’s is 8.0% lower when compared to Pre-SA. Interestingly, the shares of the total GDP for Afghanistan (Pre-SA: 0.4%; MENAAP: 0.4%) and Pakistan (Pre-SA: 7.6%; MENAAP: 7.4%) remained stable within Pre-SA and MENAAP, respectively. 

Concurrently, in the recent past, the economic performance of Afghanistan and Pakistan were in general lower relative to other economies in Pre-SA and MENAAP, impacting GDP growth rates in the regions. Over 2021-2024, the GDP growth in MENAAP is, on average, 0.1pp lower relative to Pre-MENAAP. Conversely, the GDP growth in SA is, on average, 0.5pp higher relative to Pre-SA. The effect on GDP per capita is even more pronounced: the GDP per capita for MENAAP is more than 30% lower relative to Pre-MENAAP, as the new regional group adds a large amount of population to its indicators with the inclusion of Pakistan. Meanwhile, the difference between Pre-SA and SA is +8.1%.

Labor and employment

Labor market indicators also shift with the revision in regional compositions. The youth employment-to-population ratio (ages 15–24)⁷ rises from roughly 19% in Pre-MENAAP to approximately 27% in MENAAP as Pakistan (35%) and Afghanistan (6%) represent a large share (>40%) of the youth population (144 million) in MENAAP.

At the same time, Afghanistan (32%) and Pakistan (49%) have high youth employment-to-population ratios relative to other economies in MENAAP, increasing the regional average from 19% in Pre-MENAAP to 27% in MENAAP. A similar pattern appears for the youth unemployment rate⁸: lower unemployment in Pakistan (10%) and Afghanistan (17%) drops the regional average from 25% in Pre-MENAAP to 19% in MENAAP. 

In contrast, labor market indicators in SA change only marginally, as India continues to dominate the youth population in SA (86%) and thus the population-weighted indicators. The youth employment-to-population ratio shifts from 30% in Pre-SA to 28% in SA, and the youth unemployment rate from 15% in Pre-SA to 16% in SA.

What should be considered going forward — and how can regional trends be interpreted more reliably?

Regional aggregates allow policymakers, researchers, and development practitioners to move beyond country-by-country comparisons and identify broader regional patterns. Yet, as the transition from Pre-MENAAP to MENAAP and Pre-SA to SA shows, these aggregates are sensitive to who is included in a region. Distinguishing between changes driven by underlying development dynamics — and those driven by revised regional composition — is therefore essential for producing coherent time series and for maintaining comparability.

Complementing aggregate results with country-level perspectives helps preserve contextual understanding, especially when large economies significantly influence regional averages. Sensitivity checks — such as comparing indicators before and after reclassification — provide an additional safeguard, helping analysts and policymakers interpret whether observed movements reflect real shifts or the effects of new groupings. Taken together, these practices ensure that regional statistics remain a reliable basis for dialogue, policy design, and resource allocation, even as regional boundaries evolve.

_{¹Middle East and North Africa, Afghanistan and Pakistan (MENAAP) includes the following 23 economies: Afghanistan, Algeria, Bahrain, Djibouti, Egypt, Arab Rep., Iran, Islamic Rep., Iraq, Israel, Jordan, Kuwait, Lebanon, Libya, Malta, Morocco, Oman, Pakistan, Qatar, Saudi Arabia, Syrian Arab Republic, Tunisia, United Arab Emirates, West Bank and Gaza, Yemen, Rep. Pre-MENAAP excludes Afghanistan and Pakistan.}

_{²South Asia (SA) includes the following 6 economies: Bangladesh, Bhutan, India, Maldives, Nepal, Sri Lanka. Pre-SA includes Afghanistan and Pakistan.}

_{³The age-dependency ratio compares dependents (under age 15 or age 65 and above) to the working-age population (age 15–64).}

_{⁴Defined as the ratio of younger dependents (under age 15) to the working-age population (age 15–64).}

_{⁵Defined as the ratio of older dependents (age 65 and above) to the working-age population (age 15–64).}

_{⁶Defined as the ratio of both younger dependents (under age 15) and older dependents (age 65 and above) to the working-age population (age 15-64).}

_{⁷Defined as the ratio of the population aged 15 to 24 that is employed to the total population. It indicates the share of youth who are working relative to the total youth population in that age group.}

_{⁸Defined as the share of the labor force ages 15-24 without work but available for and seeking employment.}

_{^AThe term country, used interchangeably with economy, does not imply political independence but refers to any territory for which authorities report separate social or economic statistics.}

_{^BThe World Development Indicators (WDI), including the data and reference metadata subject to this blog, are accessible via this page.}