Internet use and household financial market participation: evidence from China
Bibliographic record
Abstract
This paper examines the effect of internet use on household financial market participation and its underlying mechanism. Using data from the 2018 China Family Panel Studies (CFPS), we find that households with internet use are 4.7% more likely to participate in financial market investments than those without. The finding remains robust after accounting for endogeneity concerns using the instrumental variables approach and a fresh double machine learning approach. We highlight the phenomenon of ‘digital divide’ that the positive effects of internet use on making financial investments are more substantial among families living in economically developed regions, and families with higher income and education levels than their counterparts. We identify three novel mechanisms through which internet use affects household investment decision-making, namely the internet information mechanism, the internet social mechanism, and the internet enabling mechanism. These findings suggest that expanding digital infrastructure building and increasing the number of internet users are important ways to enhance households’ participation in financial markets.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".