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Record W3113123915 · doi:10.1111/twec.13074

Inflation targeting adoption and institutional quality: Evidence from developing countries

2020· article· en· W3113123915 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.

Bibliographic record

VenueWorld Economy · 2020
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicFiscal Policy and Economic Growth
Canadian institutionsCarleton University
Fundersnot available
KeywordsRobustness (evolution)Developing countryQuality (philosophy)Matching (statistics)Inflation targetingInflation (cosmology)EconomicsPropensity score matchingSet (abstract data type)Monetary policyPublic economicsMonetary economicsMacroeconomicsBusinessComputer scienceEconomic growthMedicine

Abstract

fetched live from OpenAlex

Abstract Institutional quality is often emphasised as an engine of economic development in developing countries. However, most of the literature assumes that institutions are exogenous. In this paper, we adopt the opposite view, and study the way the design of the monetary regime, and specifically the adoption of an inflation targeting regime, can impact the quality of institutions. Using the propensity scores matching method, which allows controlling for self‐selection in policy adoption, along with a wide set of robustness checks, including GMM‐based estimations and controlling for unobserved heterogeneity, we find that the adoption of inflation targeting significantly improves the quality of institutions.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.486
Threshold uncertainty score1.000

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 communication0.0000.001
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 head0.087
GPT teacher head0.255
Teacher spread0.168 · 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