Consumer Sentiment after the Global Financial Crisis
Bibliographic record
Abstract
The present study seeks to analyse the predictive capacity of the Index of Consumer Sentiment (ICS) (a leading index in international market research) in Italy, before and after the global financial crisis. The analysis focuses on the period 2005–2013 and investigates the predictive power of the ICS with regard to two different outcomes: (1) the actual level of household consumption (considering both its absolute value as total spending and its quarterly variations) and (2) consumers' strategies (i.e. reducing their consumption, focusing on discounts and promotions, focusing on quality), both in general and in specific sectors (e.g. food, leisure, health). The study is based on a second-level analysis of data collected by the Italian Statistical Institute (ISTAT) and a tracking survey on Italian consumers' perceptions and strategic intentions (four waves per year, each consisting of 1,000 telephone interviews based on a structured questionnaire). The findings show that the ICS is predictive of quarterly variations in household consumption, and not of its absolute values; that the index is more predictive in the following trimester, while less predictive synchronously (i.e. in the same quarter); and that its predictive power was stronger between 2009 and 2013 compared to previous years. Furthermore, after 2008, the ICS was also predictive of consumer strategies, particularly those aimed at reducing expenses and focusing on quality (while no relation seems to exist between consumer sentiment and consumers' strategies aimed at discounts and promotions). Implications for marketing and market research are discussed.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".