Appendix I Appendix for "Habit Persistence and Keeping Up with the Joneses: Evidence from Micro Data"
Bibliographic record
Abstract
The data set used in the analysis is a random sample of credit card accounts, active and not delinquent as of July 1999. Each account is followed for the period between the third quarter of 1999 and the third quarter of 2002. The unit of observation is the account in a given quarter. The card could be used by more than an individual and therefore I will refer to the decision maker behind it as household (HH). Although the original sample covers the entire U.S. territory, I restrict the analysis to California, the only state for which retail sales are available at the city level and quarterly frequency. For the same reason, although the credit card data are available at monthly frequencies, I construct quarterly variables, a choice that has also the benefit of reducing the noise that plagues individual monthly consumption data. In constructing the sample, I exclude people whose accounts are inactive and those that don’t use the card very often, in order to obtain a more meaningful measure of consumption. For an account to be in the sample the expenditure can never fall below $50 in any given quarter. The choice of this cutoff is meant to compromise between sample size and representativeness. According to Lim and Benjamin (2001), the average transaction amount on a credit card is $87, 112 % higher than that made in cash. Following the literature, I also exclude retired account holders and people living in military areas, because their expenditures are influenced by special conditions and required specific modelling that is outside the scope of this paper. After this selection
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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 | 0.002 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.669 | 0.170 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".