The Causal Nexus of Consumer and Business Confidence Indexes in Early Pandemic Period: Evidence from OECD Countries
Bibliographic record
Abstract
The COVID-19 pandemic has been shown dire consequences for the global economy, not only in the past and present but also in the future. These consequences are not only humanitarian but also financial and economic. This article raises the question of whether the state of the health system is a factor that determines the direction of changes in consumer and business sentiment during the COVID-19 or whether other factors are more significant. The goal is to find out whether there is real progress in the national health system of a particular country or a regression and on this base to answer the question: What is more important for the expectations of the population and industry during the spread of the pandemic; the dynamics of the development of the health system or other factors? To assess the dynamics of the development of the health care system in different countries, we used the annual data on individual health indicators of the OECD countries for 2006–2019. There were identified countries with dynamic development and a slowing/deteriorating health system. Based on Granger’s approach in EViews, we used the Augmented Dickey–Fuller test and admit that health care systems are not a determining factor in consumer and business sentiment during a pandemic, i.e., only economic factors. The research contributes to the developed COVID-19 research by examining the impact of the changes in the mutual influence of Confidence indexes and macro indicators during the pandemic.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".