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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 OpenAlexaff
Syed Mansoob Murshed, Brahim Bergougui, Muhammad Badiuzzaman, Mohammad Habibullah Pulok

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 sector spending captured by social protection and education spending in a cross section of developing countries, a subject on which there is scant empirical evidence. We hypothesize that fiscal capacity is necessary but not sufficient for resource allocation in this area, because the political will to do so must also be present. Using a panel data instrumental variable approach, we find that greater fiscal capacity robustly raises social spending in developing countries in the period 1990 to 2010. It is also strongly evident that rising democratisation enhances social sector spending; the presence of greater democracy and higher fiscal capacity could reinforce this effect. Our work also innovatively incorporates inequality into the analysis, finding that social expenditure is greater in more egalitarian societies. Military expenditure also appears to crowd out social protection expenditure, 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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.015
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0000.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.000

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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations43
Published2020
Admission routes1
Has abstractyes

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