Measuring Multidimensional Poverty in Conflict Zones: A Remote Sensing Method for the Tigray Conflict
Bibliographic record
Abstract
This article examines the impracticality of household surveys in conflict settings. Instead, we utilise multiple satellite data sources to generate proxy indicators of welfare, an approach that reveals significant sources for creating meaningful proxy indicators. We applied a difference-in-difference method and a tailored poverty measurement for conflict contexts to analyse causality. This analysis highlights a severe economic decline during the Tigray conflict (2019-2021). Our findings indicate that by late 2021, 94.1% of Tigray's population was living in extreme multidimensional poverty, with an intensity of 91.3%, marking a sharp increase of 71.1% in the MPI (from 0.502 ± 0.035 to 0.858 ± 0.048). We roughly attributed 82.0% ± 5.2% of this welfare decline to conflict through econometric modelling. Infrastructural collapse (69.2% reduction in nighttime light) and agricultural system failure (44.2% decrease in Normalised Difference Vegetation Index [NDVI]) are considerably more severe than what was observed in Syria (+38.0%), Yemen (+43.2%), and South Sudan (+47.5%), based on remote sensing data. Health experienced the most significant decline (+91.6%), followed by living standards (+65.8%) and education (+54.5%), according to dimensional decomposition. Our Gaussian copula modelling method illustrates how different factors are interconnected, demonstrating the relationships between them. Cohen's modelling method shows how various factors are linked, and the reliability of the estimates is indicated by comparing them to real-world evaluations in 34-42% of woredas (Cohen's κ = 0.79), as well as by assessing uncertainty through 5,000 repeated tests. We identify three distinct impacts: health-focused, infrastructure-focused, and a combination of both, by analysing data at the woreda level, which allows for targeting solutions specific to each region. In addition to documenting Tigray's humanitarian crisis, this analytical approach offers a scalable method for welfare evaluation in conflict environments with limited data worldwide, thereby strengthening evidence-based plans for humanitarian response and development.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".