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Labor market institutions, productivity, and the business cycle: An application to Italy

2025· article· en· W4410596756 on OpenAlex
Josué Diwambuena, Raquel Fonseca, Stefan F. Schubert

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

VenueEuropean Economic Review · 2025
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicLabor market dynamics and wage inequality
Canadian institutionsUniversité du Québec à MontréalGovernment of OntarioGovernment of Canada
FundersLibera Università di BolzanoUniversité du Québec à Montréal
KeywordsBusiness cycleProductivityEconomicsLabour economicsMacroeconomics

Abstract

fetched live from OpenAlex

This paper studies the effect of labor market institutions on the cyclicality of labor productivity and aggregate fluctuations in Italy when two wage bargaining protocols (efficient Nash and right-to-manage) interact with three types of hiring costs. It uses a New Keynesian model with labor market frictions and labor effort estimated with Bayesian techniques using Italian quarterly data from 1996Q1 to 2018Q4. We find that technology shocks mainly explain labor productivity fluctuations. We focus on labor market deregulation by reducing real wage rigidity, hiring costs, and workers’ bargaining power. We show that, when labor effort varies, reforms trigger procyclical productivity under efficient bargaining, and countercyclical productivity under right-to-manage bargaining. Reforms have different effects on the volatility of labor market variables. We carry out several sensitivity analyses which confirm our results.

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)0.005
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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.925
Threshold uncertainty score0.581

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.000
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 head0.020
GPT teacher head0.258
Teacher spread0.237 · 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