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Record W2291883909 · doi:10.14264/uql.2016.88

When reciprocity becomes back-scratching: an economic inquiry

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

Bibliographic record

VenueThe University of Queensland · 2016
Typedissertation
Languageen
FieldSocial Sciences
TopicExperimental Behavioral Economics Studies
Canadian institutionsnot available
Fundersnot available
KeywordsPoliticsReciprocity (cultural anthropology)ScratchingExternalityZoningValue (mathematics)DiscretionMicroeconomicsEconomicsPublic economicsBusinessPolitical scienceSocial psychologyLawPsychologyManagementStatisticsMathematics

Abstract

fetched live from 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.

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How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.013
metaresearch head score (Gemma)0.061
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.067

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.061
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0030.011
Scholarly communication0.0090.013
Open science0.0010.004
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0100.000

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.

Opus teacher head0.046
GPT teacher head0.312
Teacher spread0.266 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2016
Admission routes1
Has abstractyes

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