Effectiveness of Planning Prompts on Organizations’ Likelihood to File Their Overdue Taxes: A Multi‐Wave Field Experiment
Bibliographic record
Abstract
This paper investigates the effectiveness of planning prompts on organizations’ tax compliance behavior. We conducted a large-scale, multi‐wave field experiment examining the tax-paying behavior of all organizations that failed to file timely annual returns for a payroll tax in the province of Ontario. Organizations were randomly assigned to receive one of two letters: Ontario’s standard late notice (control) and a revised experimental late notice, which included step-by-step instructions of when, where, and how to file a return. Our data indicate that planning prompts are effective at increasing organizations’ timely tax payment. In addition to replicating these findings across two waves, we demonstrate that, although our intervention did not appear to have effects that persisted across tax years, organizations also did not habituate to our manipulation and its effects were consistent across repeated exposures. Our study is among the first to demonstrate that a simple behavioral intervention that has typically been applied to individuals to help them to act upon their existing motivations can be effective in the realm of tax compliance and organizational behavior. This paper was accepted by David Simchi-Levi, behavioral economics.
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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 | 0.006 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".