The human development index as a criterion for optimal planning
Bibliographic record
Abstract
Purpose The human development index (HDI) and gender‐related development index (GDI) have become accepted as leading measures for ranking human well being in different countries. The purpose of this paper is to identify the planning policies that improve these indices and to also suggest modifications to the indices that yield more sensible policies.Design/methodology/approach – This paper solves the first‐best welfare problem in which the planner maximizes a development index subject to resource constraints.Findings – Planning strategies that maximize the HDI tend towards minimizing consumption and maximizing expenditures on education and health. Interestingly, such strategies also tend towards equitable allocations, even though inequality aversion is not modelled in the HDI. The paper shows that the GDI generates optimal plans with similar properties, and determine when the GDI and HDI generate consistent optimal plans. A problematic feature of the optimal plans is that the income component in the HDI (or GDI) does not play its intended role of securing resources for a decent standard of living. Rather, it acts to distort the allocation between health and education expenditure. The paper argues that it is better to drop income from the index. Alternatively, the paper considers net income, income net of education and health expenditures, as indicating capabilities not already reflected in the index. Finally, it compares how the modified indices and the HDI rank countries.Originality/value – The paper is believed to be the first to integrate development indices into national development planning.
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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".