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Record W3194156785 · doi:10.1093/jeea/jvab026

Counterproductive Worker Behavior After a Pay Cut

2021· article· en· W3194156785 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueJournal of the European Economic Association · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicExperimental Behavioral Economics Studies
Canadian institutionsHEC Montréal
FundersCanada Research Chairs
KeywordsBusinessLoyaltyProductivityWork (physics)Labour economicsDemographic economicsMarketingEconomics

Abstract

fetched live from OpenAlex

Abstract We examine how workers reacted to a pay cut in a sales call center setting in the United States. The pay cut was implemented by raising two pre-existing sales targets, that is, by “moving the goalposts”. Using a difference-in-difference approach, we show that among the workers who experienced the pay cut, some chose to leave the firm (exit); others generated abnormally high customer refunds, in a way that hurt both them and the firm. (We define this work practice as counterproductive.) The firm believed, and we present evidence, that these workers intentionally sold the wrong items, as opposed to simply optimally shirking on effort in response to the pay cut. We show that the most loyal workers (those with longer tenure) expressed themselves only through counterproductive work practices and not through exit. Less loyal workers reacted more strongly than loyal workers, and did so through a balanced mix of exit and counterproductive behavior. To our knowledge, this is the first study to document individual-level patterns of exit and (counter-) productivity following a pay cut and, how these differ for high- versus low-loyalty workers.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.091
Threshold uncertainty score0.682

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.014
GPT teacher head0.278
Teacher spread0.264 · 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