A modified, implicit, directly additive demand system
Bibliographic record
Abstract
A recently developed demand system, nicknamed AIDADS (An Implicit, Directly Additive Demand System), offers an approach to capturing consumer preferences across a wide range of expenditure levels. AIDADS generalizes the LES by assuming marginal budget shares vary with utility and hence with expenditure. Like the LES, AIDADS includes subsistence parameters that define minimum consumption levels. Here we present a modified AIDADS (MAIDADS) that replaces the constant subsistence parameters with functions that also vary with utility; these transformed subsistence levels are referred to as minimum consumption quantities. This model is applied to the 1996 International Consumption Project data. As these data span a wide range of expenditure levels, MAIDADS offers a viable alternative for the estimation of a ‘global demand system’. Results suggest minimum consumption quantities for staple grains, livestock, other food products, alcohol and tobacco, clothing and footwear and transport and transport services vary with expenditure, while those for rent and fuel and household furnishings and operations are zero and invariant across expenditure levels. Only the minimum consumption quantity for staple grains declines with expenditure.
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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".