Veblen goods and neighbourhoods: endogenising consumption reference groups
Bibliographic record
Abstract
One of the significant developments in the last four decades of economics is\nthe growing empirical evidence that individual consumption preferences, as mea-\nsured by self-reported life satisfaction, are neither fixed nor self-centred but are\ninstead overwhelmingly dominated by externalities, partly in the form of reference\nlevels set by others and by one’s own experience. Welfare analysis recognising\nthis fact is likely to indicate enormous revisions for macroeconomic policy and\nsocial objectives as well as for what is taught in economics at all levels. Yet the\ntask of constructing general equilibrium models based on this microeconomic re-\nality is still in its infancy. In this work I take the conventional stance that decision\nmakers understand their own utility function. Therefore, they can choose the mi-\nlieu in which they immerse themselves with the sophisticated understanding that\nit will affect their own consumption reference levels and therefore the degree of\nsatisfaction they derive from their private consumption. At the same time, their\nprivate consumption will help to set the reference level for others in their chosen\ngroup. I treat theoretically the problem of such endogenous formation of consump-\ntion reference groups in the context of a simultaneous choice of neighbourhoods\nand home consumption amongst a heterogenous population. For both discrete and\ncontinuous distributions of types, I find general equilibrium outcomes in which\ndifferentiation of neighbourhoods occurs endogenously and I compare the welfare\nimplications of growth in such economies.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".