Essays on Industrial Organization
Notice bibliographique
Résumé
We study the question of whether women, on average, pay a price premium — a so-called“pink tax”—for the products they buy. A particular concern facing policy makers is whether\nsuch differences are a form of gender based price discrimination. Using scanner data, we find\nthat averaged across the entire retail grocery consumption basket, women pay 4% more per\nunit for goods in the same product-by-location market as do men. This price differential is\ngenerated by a 15% higher average per unit price paid by women on explicitly gendered products,\nlike personal care items, as well as a 3.8% higher average per unit price paid by women\non ungendered products, like packaged food items. Higher prices paid by women could be\nthe result of differences in demand elasticity, competitive structure, or sorting into goods\nwith differing marginal costs. To disentangle these mechanisms, we estimate demand differences\nbetween men and women and structurally decompose price differences into markups\nand marginal costs. We find that women are, on average, more price elastic consumers than\nmen, suggesting that as a consumer base women are not likely to be charged higher markups\nunder price discrimination. Overall, we find that the pink tax is not sustained by higher\nmarkups charged to women, but by women sorting into goods with higher marginal costs\nand lower markups.\nMedical provider price transparency is often touted as a key policy for efficiently lowering\nhealth care spending, which is nearly 20% of GDP. Despite its many proponents, the impact\nof price transparency is theoretically ambiguous: it could lower health care spending via\nincreased consumer price shopping or improved insurer bargaining position but could instead\nraise health care prices via improved provider bargaining or either tacit or explicit provider\ncollusion. We conduct a randomized-controlled trial to examine the impact of a state-wide\nmedical charge transparency tool in outpatient provider markets in the state of New York.\nIn the experiment, individual providers’ billed charges (list prices) were released randomly\nat the procedure X geozip level. We use a comprehensive commercial claims database to\nassess the impact of this intervention and find that the intervention causes a small increase\nin overall billed charges (+1%) but a relatively lower increase in the charges for procedures\nwith many out-of-network claims (-2%). We find no evidence for quantity effects. We find\nlarger charge increases for specific categories that are almost always insured and less elective\nin nature, e.g. MRI (+6%) and radiology (+3%) and charge decreases for categories that\nare less often insured and more elective in nature, e.g. psychology (-2%) and chiropractor\n(-3%) services. Taken together, these results are consistent with our intervention having\na minimal effect on consumer price shopping but a meaningful effect driving increases in\nproviders’ charges, especially for less elective services that are almost always covered by\ninsurance, potentially reflecting perverse price effects resulting from tacit collusion or reduced\ninformation asymmetries.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,034 | 0,048 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; les deux têtes enseignantes s’accordent sur ce qui est montré ici.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».