A philosophical analysis of the concept of an externality in economic theory and policy
Bibliographic record
Abstract
Economists generally understand externalities as unpriced spillover effects. The paradigmatic case is pollution because it is unpriced and affects agents external to the market choices that lead to its production. One solution to an externality is to set a tax on the unpriced activity at the value of the externality in equilibrium. The concept of an externality, however, is notoriously difficult to precisely define and there is a notable absence of consensus among economists. In this dissertation, I offer an analysis of the contemporary treatment of externalities in economic theory and arrive at the following definition: Externalities arise when unpriced activities generate untapped gains from exchange that are associated with untapped welfare gains. It is unclear, however, whether this concept could be instantiated in any concrete sense because gains from exchange often diverge from welfare gains. I suggest possible ways to interpret an externality given this problem, but argue that each interpretation falls short of an adequate account of externalities. The ambiguity of the concept of an externality carries over to attempts to estimate the value of a specific externality. I suggest that this accounts for some of the controversy among both economists and philosophers over one approach to estimating the value of an externality, called the contingent valuation method. Furthermore, this ambiguity renders problematic certain policies, such as the carbon tax, that are intended to internalize an externality. I then argue that the problem of climate change is not merely caused by the presence of externalities, as some economists have suggested. I argue that, even if all externalities were eliminated, a social planner might still bring about a regretful environmental state when they aim to maximize net benefit derived from polluting activities. This is a result of the peculiar cost structure of climate change in which the marginal costs are uninformative of the total costs of polluting. I suggest that, instead of aiming to balance the costs and benefits of polluting, we might need to forgo some of the potential net benefits in order to avoid reaching an irreversible and regretful state.
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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 | 0.009 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.052 |
| Scholarly communication | 0.008 | 0.022 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.007 | 0.016 |
| Insufficient payload (model declined to judge) | 0.006 | 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".