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Enregistrement W2291883909 · doi:10.14264/uql.2016.88

When reciprocity becomes back-scratching: an economic inquiry

2016· dissertation· en· W2291883909 sur OpenAlexaboutno aff
Cameron Murray

Notice bibliographique

RevueThe University of Queensland · 2016
Typedissertation
Langueen
DomaineSocial Sciences
ThématiqueExperimental Behavioral Economics Studies
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésPoliticsReciprocity (cultural anthropology)ScratchingExternalityZoningValue (mathematics)DiscretionMicroeconomicsEconomicsPublic economicsBusinessPolitical scienceSocial psychologyLawPsychologyManagementStatisticsMathematics

Résumé

récupéré en direct d'OpenAlex

This thesis reports four studies of a particular type of cooperation where the formation of coordinated groups through favour exchanges benefits the connected few at the expense of the many. This process is labelled back-scratching, and is a common feature of political decision-making where institutional powers allow for a large amount of discretion and the imposition of external- ities in situations where property rights are not well-defined. Chapter 1 introduces the concept of back-scratching in as a coordination game with negative externalities, providing a common framework within which to incorporate the studies that follow. The first study in Chapter 2 uses a natural experiment to quantify the gains from back-scratching in political decisions about value-enhancing land zoning. The effectiveness of a variety methods used to support implicit favouritism are examined, including political donations, employing professional lobbyists, and investing in relationships. Using micro-level relationship data from multiple sources, characteristics of landowners of comparable sites inside and outside rezoned areas are compared. ‘Connected’ landowners owned 75% of land inside rezoned areas, and only 12% outside, and captured $410 million in value gains, indicating a trade in favours amongst con- nected insiders. Marginal gains to all landowners of connections in our sample were $190 million. Engaging a professional lobbyist appears to be a substitute for having one’s own connections. The second study in Chapter 3 offers a theoretical explanation for the unusual hedging and partisan patterns of political donations observed in Australia, Canada, UK and Germany based on a model of donations as reputation signals, and where reputation levels determine the political distribution of the economic surplus. Simulating optimal signal investments in a population of agents distributed within a reputation space results in a clustering of signalling strategies consistent with political donations data. The model shows how the entrenchment of interests can occur through exclusive access to a ‘social ladder’ for elites engaged signalling reputations, offering a potential underlying explanation of Mancur Olson’s (1982) institutional sclerosis. To explore more closely potential institutional changes to curtail back-scratching a new experiment is introduced in Chapter 4 that allows for back-scratching between player pairs to arise within a group of four players. In each of the 25 rounds of the experiments, a player (the ‘allocator’) nominates one of three others as a co-worker (the ‘receiver’), which determines the group production that period to be the productivity of the receiver (which varies by round), but also gives the receiver a bonus and makes them the allocator in the next round. Alliances form if two individuals keep choosing each other even when their productivities are lower than that of others, causing efficiency losses; a situation that occurred in 84% of experiment groups. Males and business students were found to be more likely to form alliances. Random allocator rotation policies and low bonuses fail to significantly improve overall welfare: rotation policies significantly reduce the rate of formation of new alliances but do not lead to the breakdown of existing alliances, while low bonus policies are only found to be effective when alliances are well established. This points to the importance of the strength of existing alliances for the chances of institutional interventions curtailing back-scratching. Institutional changes creating greater transparency are tested in the new experimental setup and reported in Chapter 5. The main treatment reveals photographs of each player in order to deter bilateral alliances and encourage cooperation with the group as a whole in the absence of punishment. Transparency does not affect the probability of alliance formation due to two countervailing forces; more rapid alliance formation due to the use social cues from the photos as a coordination device, and more pro-sociality at the group level that leads to shorter alliances. There are policy lessons about when transparency may curtail corruption, or facilitate it.

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesCharge utile insuffisante (le modèle a refusé de juger)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Qualitatif · Signal consensuel: Qualitatif
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,190
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0010,000
Communication savante0,0000,000
Science ouverte0,0010,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0010,000

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.

Tête enseignante Opus0,046
Tête enseignante GPT0,312
Écart entre enseignants0,266 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Devis d'étudeQualitatif
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations1
Publié2016
Routes d'admission1
Résumé présentoui

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