The Advantages of Probabilistic Survey Questions
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In monetary policymaking, central bankers have long pointed out the importance of measuring the expectations of financial market participants, households, and firms —especially with regard to inflation and the central bank’s so-called “reaction function” to changes in the economic outlook. In addition to model- and market-implied measures, there has been a growing interest in and reliance on survey-based measures of subjective expectations. This article describes two major innovative survey initiatives conducted by the New York Fed to measure policy-relevant expectations of households and market participants: the Survey of Consumer Expectations, and the Survey of Primary Dealers and Survey of Market Participants. A key feature of these surveys is its use of a probabilistic question format to elicit the likelihood respondents assign to different future events. We discuss the advantages of using probabilistic questions, illustrate their value in more fully measuring beliefs and uncertainty, and document the pervasiveness and importance of heterogeneity in beliefs among our survey respondents.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.051 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it