Using discrete choice experiments to value informal care tasks: exploring preference heterogeneity
Bibliographic record
Abstract
While informal care is a significant part of non-market economic activity, its value is rarely acknowledged, perhaps reflecting a lack of market data. Traditional methods to value such care include opportunity and replacement cost. This study is the first to employ the discrete choice experiment methodology to value informal care tasks. A monetary value is estimated for three tasks (personal care, supervising and household tasks). The relationship between time spent on formal and informal care is also modelled and preference heterogeneity investigated using the Latent Class Model. Complementarity between supervising tasks and formal care is observed. Monetary compensation is important, with willingness to accept per hour values ranging from £0.38 to £0.83 for personal care, £0.75 for supervising and £0.31 to £0.6 for household tasks. Heterogeneity in preferences is observed, with monetary compensation being important for younger people, but insignificant for older individuals. Such heterogeneity is important at the policy level. Values are lower than those generated by opportunity cost and replacement cost methods, perhaps because of the limited ability of revealed preference methods to capture broader aspect of utility. Differences with contingent valuation methods are also observed, suggesting future research should investigate the external validity of the different methods.
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 | 0.044 | 0.096 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".