Sagaing (MPI score 0.196) has approximately 2.38 million MPI-poor people. Its poverty profile reflects the compounding of longstanding structural disadvantages, namely weak rural infrastructure, agricultural dependency, and limited service coverage, with acute conflict-related disruption that has fractured livelihoods and displaced communities across the region. Ayeyarwady has the largest MPI-poor population nationally (approximately 2.65 million), yet its comparatively moderate severity (MPI score 0.183) indicates a poverty profile shaped more by long-standing agrarian conditions than acute conflict-driven crisis. A large agrarian population with limited livelihood diversification, high exposure to natural hazard shocks, and constrained access to services explains why Ayeyarwady contributes so substantially to national poverty counts despite not recording the highest deprivation levels. High severity, small scale: Kayah, Chin, Kachin, Kayin These states record above-average MPI severity but have smaller populations, so their aggregate contribution to national poverty counts is more limited. The defining analytical feature of this quadrant is the disjuncture between the depth of deprivation and the scale of the affected population. Kayah records the second-highest MPI score nationally (0.328) with a headcount of 68.1 percent, yet only approximately 0.21 million MPI-poor people. The concentration of severe outcomes within a small population makes Kayah’s poverty profile one of severity rather than scale, a pattern that distinguishes it analytically from high-severity, high-scale contexts such as Rakhine. Chin (MPI score 0.220) has approximately 0.25 million MPI-poor people. Poverty here reflects structural isolation, such as limited road connectivity, weak service infrastructure, and geographic barriers to market integration, rather than conflict-driven deterioration alone, though the two interact in parts of the state. Kachin (MPI score 0.188) has approximately 0.81 million MPI-poor people. The state’s overall severity sits above the national average, yet remains lower than Kayah and Chin, reflecting a poverty profile shaped by the intersection of conflict-related disruption and geographic isolation across rural and border areas. Kayin (MPI score 0.153) has approximately 0.51 million MPI-poor people. Its position near the quadrant boundary reflects a poverty profile where conflict-related disruption overlaps with structural access constraints, producing moderate but persistent deprivation across a relatively small population. Low severity, large scale: Mandalay, Shan, Bago, Magway, Yangon These states and regions record below-average MPI severity but above-average numbers of MPIpoor people, making them the primary drivers of the national poverty headcount. Aggregate poverty burdens here reflect scale effects rather than the depth of individual household deprivation. Mandalay (MPI score 0.148) and Shan (MPI score 0.131) each contain approximately two million MPI-poor people. Despite moderate severity, their population size means poverty outcomes carry considerable weight in national aggregates. Bago and Magway similarly combine moderate MPI scores (0.118 and 0.128 respectively) with sizeable affected populations of approximately 1.34 million and 1.17 million, driven largely by structural rural livelihood vulnerabilities and uneven service coverage. Yangon presents a particularly distinctive profile within this quadrant. An MPI headcount of 20.4 percent (the third-lowest nationally) translates into approximately 1.73 million MPI-poor people within L AYE R S O F DE P R I VAT I O N : M u l t i di m en si on a l Pov er t y i n M y anmar 13