Assessing the Accuracy of Non-Random Business Conditions Surveys: A Novel Approach
Bibliographic record
Abstract
Summary A number of central banks and other institutions publish their own business conditions surveys that rely on complex non-probability sampling methods. The results of these surveys influence policy decisions and affect expectations in financial markets. To date, no one has assessed the accuracy of these surveys because their complex (and often unique) sampling method renders this assessment non-trivial. The paper describes a novel approach for modelling unique sampling methods when many constraints (including quota sampling and clustering) are imposed. When no closed form solution exists, we show how to compute the selection probabilities from each firm in the known population and the dispersion of the sampling distribution by using Monte Carlo techniques. This method can also be used to assess the appropriateness of a survey sample size. Our approach is applicable to many major surveys conducted by various central banks and institutes across the Organisation for Economic Co-operation and Development. To demonstrate the feasibility of our approach, we apply it to the Bank of Canada’s ‘Business outlook survey’. Although the survey’s coverage is significantly limited, we find, under certain assumptions, no evidence that the Bank of Canada’s firm selection process results in a wider dispersion in the sampling distribution than the stratified random sample.
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How this classification was reachedexpand
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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".