Factors Driving the Credit Card Ownership in Italy
Bibliographic record
Abstract
<p>The aim of this paper is to explore the main determinants of credit card ownership by analyzing the variables that may affect the decision to hold a credit card. Using the 2012 Survey on Household Income and Wealth provided by the Bank of Italy as main source, we estimated count data models in order to identify the socio-economic, demographic and territorial variables affecting the credit card accounts held by households. Our estimates give evidence of the significance of the considered factors. In particular, we find that geographical location is an important determinant of families behavior in line with the socio-economic gap between the North and the South of Italy. Other relevant variables acting on the number of credit cards held are age, income, municipality size, gender, education and marital status. The reached results are interesting in depicting the main characteristics of cardholders and in helping the implementation of a segment-specific marketing program in the banking services industry.</p>
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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.
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".