Experimental Tests of Survey Responses to Expenditure Questions
Bibliographic record
Abstract
Abstract This paper tests for a number of survey effects in the elicitation of expenditure items. In particular, we examine the extent to which individuals use features of the expenditure question to construct their answers. We test whether respondents interpret question wording as researchers intend and examine the extent to which prompts, clarifications and seemingly arbitrary features of survey design influence expenditure reports. We find that over one‐quarter of respondents have difficulty distinguishing between ‘you’ and ‘your household’ when making expenditure reports; that respondents report higher pro‐rata expenditure when asked to give responses on a weekly as opposed to annual timescale; that respondents give higher estimates when using a scale with a higher midpoint; and that respondents report higher aggregated expenditure when categories are presented in a disaggregated form. In summary, expenditure reports are constructed using convenient rules of thumb and available information, which will depend on the characteristics of the respondent, the expenditure domain and features of the survey question. It is crucial to further account for these features in ongoing surveys.
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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 | 0.026 | 0.195 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.027 | 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".