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Record W4407008630 · doi:10.1080/00220388.2025.2451868

Employer and Employee Preferences for Worker Benefits: Evidence from a Matched Employer-Employee Survey in Bangladesh

2025· article· en· W4407008630 on OpenAlex
Krishna B. Kumar, Minhaj Mahmud, Shanthi Nataraj, Yoonyoung Cho

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.

Bibliographic record

VenueThe Journal of Development Studies · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicGender, Labor, and Family Dynamics
Canadian institutionsImpact
FundersRAND Corporation
KeywordsEmployee engagementBusinessEmployee researchEmployee resource groupsLabour economicsDemographic economicsEconomicsManagement

Abstract

fetched live from OpenAlex

We study the working conditions of low-skilled workers in a developing country, Bangladesh, to shed light on the conditions workers value and employers may be willing to provide. Using a carefully designed choice experiment embedded in a unique survey of employers and employees, we elicit preferences for compensation, leave and termination policies, working hours, overtime pay, and accident compensation. Workers value termination notice and accident compensation, and employers are not averse to providing them. However, workers find long working hours without overtime compensation to be highly undesirable, whereas many employers are unwilling to provide shorter hours or overtime pay unless they face the threat of fines or are offered substantial incentives. Our findings suggest that encouraging the provision of termination notice and accident compensation may be relatively easy, but that increasing compliance with legal limits on working hours and overtime compensation is likely to require increased enforcement or substantial incentives.

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)0.004
metaresearch head score (Gemma)0.002
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 score0.040
Threshold uncertainty score0.979

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.118
GPT teacher head0.348
Teacher spread0.229 · 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