Commitment to marketing spending through recessions
Bibliographic record
Abstract
Purpose This paper aims to address two unique and important questions. First, how do recessions directly affect firms’ marketing spending decisions? Second, and more importantly, do firms which are more committed to marketing spending through past recessions achieve better stock market returns? Design/methodology/approach This study is based on a combination of National Bureau of Economic Research, COMPUSTAT and Center for Research in Security Prices data on 6,000 firms between 1982 and 2009 which are analyzed using panel data-based regression models. Findings The authors find that firms cut marketing spending during recessions. However, firms committed to marketing spending during past recessions achieve better stock market returns. The findings are found to be robust across B2B and B2C industries, different periods and US firms which vary on the proportion of their global revenue from non-US sales. Research limitations/implications Top executives cut marketing budgets during recessions; however, if they can resist the pressures, and strategically continue to make marketing investments during recessions, they will achieve higher stock market returns. Originality/value This is the first paper to establish the longer-term (not short-term) positive stock market performance of continuous (not episodic) marketing spending through past recessions, i.e. the view that marketing spending is necessary (not discretionary) for stock returns.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".