Measuring the Impact of Free Goods on Real Household Consumption
Bibliographic record
Abstract
A puzzling development over the past 15 years is decline in Total Factor Productivity in many advanced economies. Part of this decline may be due to the rapid growth of free digital goods. Statistical agencies have no reliable way to measure the benefits of the introduction of free goods. This is true even when the provision of the goods is paid for via advertising. Yet these free goods are enormously popular and surely create substantial utility for households. In this paper, we suggest a methodology which will allow statistical agencies to form rough approximations to the benefits that flow to households from new free goods. The present paper draws heavily on the contributions of Brynjolfsson, Collis, Diewert, Eggers and Fox (2019) (subsequent references will be to BCDEF) and Diewert, Fox and Schreyer (2019). In section I, we outline how the reservation price methodology introduced by Hicks (1940; 114) can be used to measure the consumption benefits to households of new products that are provided at zero cost or costs that are close to zero. This Hicksian approach relies on normal index number theory but requires the estimation of reservation prices. In section II, we show how choice experiments about compensation for product withdrawals can be used to estimate these reservation prices. Section III concludes with a summary and implications.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".