The Impact of the Internet on Advertising Markets for News Media
Bibliographic record
Abstract
In this paper, we explore the hypothesis that an important force behind the collapse in advertising revenue experienced by newspapers over the past decade is the greater consumer switching facilitated by online consumption of news.We introduce a model of the market for advertising on news media outlets whereby news outlets are modeled as competing two-sided platforms bringing together heterogeneous, partially multi-homing consumers with advertisers with heterogeneous valuations for reaching consumers.A key feature of our model is that the multi-homing behavior of the advertisers is determined endogenously.The presence of switching consumers means that, in the absence of perfect technologies for tracking the ads seen by consumers, advertisers purchase wasted impressions: they reach the same consumer too many times.This has subtle effects on the equilibrium outcomes in the advertising market.One consequence is that multi-homing on the part of advertisers is heterogeneous: high-value advertisers multi-home, while low-value advertisers single-home.We characterize the impact of greater consumer switching on outlet profits as well as the impact of technologies that track consumers both within and across outlets on those profits.Somewhat surprisingly, superior tracking technologies may not always increase outlet profits, even when they increase efficiency.In extensions to the baseline model, we show that when outlets that show few or ineffective ads (e.g.blogs) attract readers from traditional outlets, the losses are at least partially offset by an increase in ad prices.Introducing a paywall does not just diminish readership, but it furthermore reduce advertising prices (and leads to increases in advertising prices on competing outlets).
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 0.001 |
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".