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Record W2999544849 · doi:10.5089/9781513525846.001

The (Subjective) Well-Being Cost of Fiscal Policy Shocks

2020· article· en· W2999544849 on OpenAlex
Kodjovi Eklou, Mamour Fall

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

VenueIMF Working Paper · 2020
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicFiscal Policy and Economic Growth
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsDisinflationEconomicsMonetary economicsFiscal adjustmentConsolidation (business)Fiscal policyExchange rateFiscal unionInternational economicsMacroeconomicsFinance

Abstract

fetched live from OpenAlex

Do discretionary spending cuts and tax increases hurt social well-being? To answer this question, we combine subjective well-being data covering over half a million of individuals across 13 European countries, with macroeconomic data on fiscal consolidations. We find that fiscal consolidations reduce individual well-being in the short run, especially when they are based on spending cuts. In addition, we show that accompanying monetary and exchange rate policies (disinflation, depreciations and the liberalization of capital flows) mitigate the well-being cost of fiscal consolidations. Finally, we investigate the well-being consequences of the two well-knowns expansionary fiscal consolidations episodes taking place in the 80s (in Denmark and Ireland). We find that even expansionary fiscal consolidations can have well-being costs. Our results may therefore shed some light on why some governments may choose to consolidate through taxes even at the cost of economic growth. Indeed, if spending cuts are to generate a large well-being loss, they can trigger an opposition and protest against a fiscal consolidation plan and hence making it politically costly.

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)0.000
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 score0.892
Threshold uncertainty score0.715

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0000.001

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 head0.029
GPT teacher head0.228
Teacher spread0.199 · 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