Investigation of Household Debt through Multilevel Multivariate Analysis: Case of a Developing Country
Bibliographic record
Abstract
This study focuses on investigating the relationships between different socioeconomic and demographic characteristics and households’ debt decision and demand. We used six survey rounds of data from Pakistan household integrated expenditure survey (HIES) 2001 to 2014. HIES is a nationally representative data collected by Pakistan Bureau of Statistics. Multilevel models were used to investigate the relationship in which the data on households was nested in primary sampling units (PSUs) and PSUs were nested in provinces. The decision of taking household debt varies 22% at PSU level and 18% at provincial level due to unobserved variables. We found that households having higher financial assets, higher income and larger household sizes tend to have a higher percentage of debt. The amount of debt also increases with education and age. In the case of demand for debt, the variation is 12% at the provincial level. Literature studying household debt decision in Pakistan often ignore the geographical differences (region/province specific studies). Considering socioeconomic characteristics habituating the usage of credit is of countless importance in guiding policy design and interventions that aim to improve financial inclusion.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".