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Record W3086637210 · doi:10.1080/10242694.2020.1817259

Fiscal Capacity, Democratic Institutions and Social Welfare Outcomes in Developing Countries

2020· article· en· W3086637210 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

VenueDefence and Peace Economics · 2020
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicFiscal Policy and Economic Growth
Canadian institutionsNova Scotia Health Authority
FundersUnited Nations University World Institute for Development Economics Research
KeywordsEconomicsSocial protectionSocial WelfareInequalityDemocratizationPanel dataPublic economicsDeveloping countryDemocracyEconomic inequalityPoliticsWelfareFiscal capacityDevelopment economicsEconomic growthPolitical scienceEconometrics

Abstract

fetched live from OpenAlex

The purpose of this paper is to gauge the various determinants of social
\nsector spending captured by social protection and education spending in
\na cross section of developing countries, a subject on which there is scant
\nempirical evidence. We hypothesize that fiscal capacity is necessary but
\nnot sufficient for resource allocation in this area, because the political will
\nto do so must also be present. Using a panel data instrumental variable
\napproach, we find that greater fiscal capacity robustly raises social spending in developing countries in the period 1990 to 2010. It is also strongly
\nevident that rising democratisation enhances social sector spending; the
\npresence of greater democracy and higher fiscal capacity could reinforce
\nthis effect. Our work also innovatively incorporates inequality into the
\nanalysis, finding that social expenditure is greater in more egalitarian
\nsocieties. Military expenditure also appears to crowd out social protection
\nexpenditure, but not robustly

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.574
Threshold uncertainty score0.881

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)0.0000.000

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 head0.086
GPT teacher head0.242
Teacher spread0.156 · 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