A Note on Monitoring Daily Economic Activity Via Electronic Transaction Data
Bibliographic record
Abstract
Economists have traditionally relied on monthly or quarterly data supplied by central statistical agencies for macroeconomic monitoring. However, technological advances of the past several years have resulted in new high-frequency data sources that could potentially provide more accurate and timely information on the current level of economic activity. In this paper we explore the usefulness of electronic transactions as real-time indicators of economic activity, using Canadian debit card data, and using two potentially important economic events as examples. In particular we are able to analyze expenditure patterns around the September 11 terrorist attacks and the August 2003 electrical blackout, and are able to note qualitative differences in the effects of these events which could not be observed through aggregate measures. Les économistes se sont traditionnellement appuyés sur les données mensuelles ou trimestrielles publiées par les agences centrales de statistiques pour suivre la situation macroéconomique. Cependant, les avancées technologiques qui ont été réalisées au cours des dernières années ont entraîné de nouvelles sources de données à haute fréquence, et ces dernières pourraient potentiellement donner lieu à une information plus exacte et plus opportune sur l'état actuel de l'activité économique. Dans le document actuel, nous explorons l'utilité des transactions électroniques comme indicateurs en temps réel de l'activité économique. Pour ce faire, nous recourons aux données canadiennes sur les cartes de débit et utilisons, à titre d'exemples, deux événements économiques susceptibles d'être importants. Plus particulièrement, nous sommes en mesure d'analyser la structure des dépenses lors des attaques terroristes du 11 septembre et de la panne d'électricité d'août 2003 et de noter des différences qualitatives dans les répercussions de ces événements, lesquelles ne pourraient être observées en recourant aux mesures globales.
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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 | 0.009 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".