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Record W2051269138 · doi:10.1007/s10683-006-9148-7

Dissertation abstract: “Essays in applied economics on the intervention of a third player in agency relationships”

2007· article· en· W2051269138 on OpenAlexaffabout
Nicolas Jacquemet

Bibliographic record

VenueExperimental Economics · 2007
Typearticle
Languageen
FieldSocial Sciences
TopicCorruption and Economic Development
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsDelegationIncentiveLanguage changePrincipal (computer security)Principal–agent problemAgency (philosophy)EconomicsPublic economicsMicroeconomicsHealth careIntervention (counseling)Deterrence theoryBusinessPolitical scienceLawCorporate governancePsychologyFinanceComputer security

Abstract

fetched live from OpenAlex

Abstract Agency theory has established that appropriate incentives can reconcile the diverging interests of the principal and the agent. Focusing on three applications, this dissertation evaluates the empirical relevance of these results when a third party interacts with the primary contract. The analyses provided rely on either laboratory or natural experiments. First, corruption is analyzed as a two-contract situation: a delegation contract between a Principal and an Agent and a corruption pact concluded between this Agent and a third player, called Briber. A survey of the recent microeconomic literature on corruption first highlights how corruption behavior results from the properties of those two agreements. We thereafter show that the Agent faces a conflict in reciprocities due to those two conflicting agreements. The resulting delegation effect, supported by observed behavior in our three-player experimental game, could account for the deterrence effect of wages on corruption. Second, health care is governed by contradictory objectives: patients are mainly concerned with the health provided, whereas containing health care costs is the primary goal of health care administrators. We provide further insights into the ability of incentives to balance these two competing objectives. In this matter, our theoretical and econometric analysis evaluates how a new mixed compensation scheme, introduced in Quebec in 1999 as an alternative to fee-for-services, has affected physicians’ practice patterns. Free switching is shown to be an essential feature of the reform, since it implements screening between physicians. Finally, the demand for underground work departs from the traditional Beckerian approach to illegal behavior, due to the dependence of benefits from illegality on competitors’ behavior. We set up a theoretical model in which the demand for underground work from all producers competing on the same output market is analyzed simultaneously. We first show that competition drastically undermines the individual benefits of tax evasion. At equilibrium, each firm nonetheless chooses evasion with a positive probability, strictly lower than one. This Bertrand curse could then account for the “tax evasion puzzle” i.e. the overprediction of evasion in models that ignore market interactions. We thereafter show that allowing firms to denounce competitors’ evasion is not likely to solve this curse—by providing a credible threat against price cuts, it fosters illegal work. Empirical evidence from a laboratory experiment confirms these predictions. Without denunciation, experimental firms often choose evasion whereas evasion benefits are canceled out by competition. When introduced, denunciation is rarely used by firms, but the threat makes evasion profitable.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

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)0.006
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.010
Threshold uncertainty score0.039

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.010
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0020.010
Scholarly communication0.0040.004
Open science0.0010.001
Research integrity0.0040.004
Insufficient payload (model declined to judge)0.0100.002

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 head0.049
GPT teacher head0.327
Teacher spread0.278 · 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 designTheoretical or conceptual
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

Citations0
Published2007
Admission routes2
Has abstractyes

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