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Record W4205711338 · doi:10.1561/113.00000040

Populist Support, Institutional Trust, and Gender: The Impact of Foreign-Imposed Austerity During the European Debt Crisis

2021· article· en· W4205711338 on OpenAlexaff
Beatrice Magistro, Lucas N. Owen, Nicolas Wittstock

Bibliographic record

VenueJournal of Political Institutions and Political Economy · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicSocial Policy and Reform Studies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsAusterityEuropean debt crisisDebt crisisFinancial crisisDebtFinancial systemBusinessEconomic policyPolitical scienceEconomicsEuropean unionEuropean integrationMacroeconomicsFinancePolitics

Abstract

fetched live from OpenAlex

What explains the recent rise of populism in Europe? We argue that economic shocks benefit populists when foreign interests influence establishment politicians and lead voters to reject the establishment response. Using a difference-in-differences design with European Social Survey data, we test this theory by comparing countries granted implicitly or explicitly conditional loans from the EU and IMF to other countries, before and after the European sovereign debt crisis. Since recent studies suggest austerity impacted women more, we also explore heterogeneity in treatment effects by gender. Supporting our theory, we find conditionality reduced institutional trust and increased populist support. Conditionality did not, however, lead to larger declines in trust or greater increases in populist support for women than for men. These results suggest that, though economic theories of voting do explain part of the recent rise in populism, men and women are not equally persuaded by economic reasons to support populists.

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 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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.488
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.003
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 head0.066
GPT teacher head0.366
Teacher spread0.300 · 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; both teacher heads agree on what is shown here.

Study designTheoretical or conceptual
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

Citations2
Published2021
Admission routes1
Has abstractyes

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