The Determinants of Limited Household Participation in Risky Financial Markets: Evidence from China Using Explainable Machine Learning
Bibliographic record
Abstract
This study takes the limited household participation in risky financial markets as its point of departure. Drawing on microdata from the 2019 China Household Finance Survey (CHFS), we construct a multidimensional analytical framework using machine learning methods. The results indicate that this limitation arises from the interplay of multiple dimensions, with significant nonlinear relationships observed between these factors and household investment behavior. Insufficient development of key driving factors constitutes the main barrier to participation in risky financial markets. Feature interaction analysis reveals a “reversal effect” in how urban–rural disparities, economic attention, income level, and social engagement shape participation behavior. Educational attainment and financial literacy act as “threshold conditions” that enable economic attention to translate into actual investment decisions. The heterogeneity analysis further shows that households at different life-cycle stages as well as across urban–rural settings exhibit distinct participation patterns. These findings provide data-driven insights that can inform policies to promote financial inclusion, enhance investor education, and strengthen household risk management practices.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".