Crisis management: personal financial well-being and public attitudes toward government intervention
Bibliographic record
Abstract
Purpose Crises precipitate strong fiscal responses by government – sometimes toward austerity, other times toward renewed social spending. This variation in approaches to crisis handling has the potential to highlight factors that drive public opinion toward government interventions that may be quite different from those in non-crisis times. This study aims to discuss the aforementioned issues. Design/methodology/approach This article brings together theories of government policymaking in crises, policy responsiveness and economic voting to assess how personal financial (egocentric) concerns and/or national financial (sociotropic) concerns may influence opinions toward government handling of direct financial supports in a crisis and, more generally, opinions toward social policy interventions. The authors assess this dynamic in the Canadian context using original national survey data collected in the initial stage of the pandemic-based crisis in June and July of 2020 (N = 1290). Findings The authors find strong evidence in support of sociotropic concerns shaping government approval and support for greater social policy interventions, but limited evidence to support egocentric concerns, suggesting that social policy attitudes may be more insulated from personal factors than anticipated. Research limitations/implications The authors’ findings suggest that crises may prompt enhanced support for interventionist social policy measures that may lack broad-based support in non-crisis times. Originality/value The authors’ findings speak to the ongoing discussion around the possibility for crises to function as policy windows for enhanced social spending and for entrenching targeted financial supports for vulnerable individuals.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".