Measuring and Decomposing Inequality among the Multidimensionally Poor Using Ordinal Data: A Counting Approach
Bibliographic record
Abstract
Poverty has many dimensions, which, in practice, are often binary or ordinal in nature.A number of multidimensional measures of poverty have recently been proposed that respect this ordinal nature.These measures agree that the consideration of inequality across the poor is important, which is typically captured by adjusting the poverty measure to be sensitive to inequality.This, however, comes at the cost of sacrificing certain policy-relevant properties, such as not being able to break down the measure across dimensions to understand their contributions to overall poverty.In addition, compounding inequality into a poverty measure does not necessarily create an appropriate framework for capturing disparity in poverty across population subgroups, which is crucial for effective policy.In this paper, we propose using a separate decomposable inequality measure -a positive multiple of variance -to capture inequality in deprivation counts among the poor and decompose across population subgroups.We provide two illustrations using Demographic Health Survey datasets to demonstrate how this inequality measure adds important information to the adjusted headcount ratio poverty measure in the Alkire-Foster class of measures. Seth and Alkire Inequality among the Multidimensionally Poor using Ordinal DataThe Oxford Poverty and Human Development Initiative (OPHI) is a research centre within the Oxford Department of International Development, Queen Elizabeth House, at the University of Oxford.Led by Sabina Alkire, OPHI aspires to build and advance a more systematic methodological and economic framework for reducing multidimensional poverty, grounded in people's experiences and values.
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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 | 0.010 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| 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".