Essays on Advertising Spending During the Great Recession and Real Earnings Management Using Advertising Budgets
Bibliographic record
Abstract
In my main dissertation essay, I investigate advertising spending during a recession. Advertising plays an important role in creating awareness, preference and purchase intent for many products and services. However, advertising is often cut when a firm needs to control costs. This empirical study examines a unique set of factors which motivated 553 firms to change their advertising spending during the Great Recession. The first half of the Great Recession had a moderate 2% decline in GDP and 1% to 2% cuts in advertising spending. The seasonality effect was weaker, which indicates that firms were not as likely to carryover spending from the prior year. The peak of the Great Recession had a GDP decline as high as 7%, which is considered severe. Average advertising spending declined by 13%. In addition to the seasonality effect, decreasing sales decreased advertising spending. Increasing firm risk tends to decrease advertising spending during the peak of the Great Recession, but not before. Finally, firms in high advertising intensity industries, where advertising is strategically important, had modest budget cuts. In contrast, firms in low-intensity industries had much larger percentage cuts.The second essay examines real earnings management using advertising budgets” examines. Real earnings management occurs when managers change real activities to meet or beat important earnings benchmarks. Advertising has a limited short-term impact on firm sales for many products. Therefore, when a firm’s earnings are below key benchmarks for a fiscal quarter (year), managers are compelled to reduce advertising expenditures to boost earnings. This study examines factors which persuade firms to manage earnings using advertising budgets. Similar to earlier studies, we find firms suspect of managing earnings upwards reducing advertising expenses. The findings indicate that B2C firms are more likely to manage earnings by reducing advertising expenses than B2B firms. The findings also reveal that suspect firms which spend more in high advertising elasticity mediums such as TV do not reduce advertising spending as much as firms which spend more in low advertising elasticity mediums such as newspapers and magazines. The study also find evidence to suggest that suspect firms which report advertising expenditure in their income statement make smaller advertising spending cuts than firms which don’t report advertising expenditure. Finally, earnings management activity is much stronger during the last quarter of the fiscal year.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".