Durable Products with Multiple Used Goods Markets: Product Upgrade and Retail Pricing Implications
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Used goods markets are currently important transaction channels for durable products. For some durable products, such markets first appeared when retailers started buying back used products from “old” customers and selling them to new ones for a profit (retail used goods market). The growth of electronic peer-to-peer (P2P) markets opened up a second, frictionless used goods channel where new customers can buy used products directly from old customers (P2P used goods market). Both these markets compete with the original primary market where retailers sell unused products procured from the manufacturer. This paper focuses on understanding the role that the sequential emergence of the above two used goods markets plays in shaping the product upgrade strategy of the manufacturer and the pricing strategy of the primary market retailer in the context of a decentralized, dyadic channel dealing with a renewable set of consumers. Our analysis establishes that frequent product upgrades and rising retail prices in durable product sectors of our interest are due to the emergence of the P2P used goods market and how the market interacts with the retail used goods source in altering the relative powers of the channel partners. Moreover, contrary to popular belief, we show that the initial introduction of the retail used goods channel actually discourages introduction of new versions and restrains the rise in retail prices. We also comment on how the two used goods markets affect the profits of the channel partners. We then provide empirical support for our theoretical result regarding product upgrades using data from the college textbook industry, a durable product that fits our model setup.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.006 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it