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Record W1700073873 · doi:10.1177/0010414015594627

Political Parties, Clientelism, and Bureaucratic Reform

2015· article· en· W1700073873 on OpenAlexaff
Cesi Cruz, Philip Keefer

Bibliographic record

VenueComparative Political Studies · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicCorruption and Economic Development
Canadian institutionsUniversity of British Columbia
FundersUniversität HamburgWorld Bank Group
KeywordsClientelismPoliticsIncentiveBureaucracyPublic sectorGovernment (linguistics)Public administrationEconomicsPolitical economyPolitical scienceMarket economyDemocracyLawEconomy

Abstract

fetched live from OpenAlex

The challenge of public administration reform is well known: Politicians often have little interest in the efficient implementation of government policy. Using new data from 439 World Bank public sector reform loans in 109 countries, we demonstrate that such reforms are significantly less likely to succeed in the presence of non-programmatic political parties. Earlier research uses evidence from a small group of countries to conclude that clientelist politicians resist reforms that restrict their patronage powers. We support this conclusion with new evidence from many countries, allowing us to rule out alternative explanations, including the effect of electoral and political institutions. We also examine reforms that have not been the subject of prior research: those that make public sector financial management more transparent. Here, we identify a second mechanism through which non-programmatic parties undermine public sector reform: Clientelist politicians have weaker incentives to exercise oversight of policy implementation by the executive branch.

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)0.007
metaresearch head score (Gemma)0.040
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 score0.014
Threshold uncertainty score0.045

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.040
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.004
Science and technology studies0.0030.006
Scholarly communication0.0040.002
Open science0.0010.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0140.001

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 head0.424
GPT teacher head0.472
Teacher spread0.048 · 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

Citations96
Published2015
Admission routes1
Has abstractyes

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