The impact of financial intermediation by BPRs, commercial banks, and fintech P2P lending on per capita consumption, MSME production, and GRDP in Indonesia
Bibliographic record
Abstract
We investigate how financial intermediation by rural banks (BPRs), commercial banks, and fintech P2P lending influences per-capita household consumption, MSME production, and Gross Regional Domestic Product (GRDP) across Indonesia’s 34 provinces during 2022–2024 (N = 102). Using Partial Least Squares Structural Equation Modeling (PLS-SEM) via SmartPLS with bootstrapped inference, we estimate a system of simultaneous equations that captures both direct and indirect (mediated) pathways from each intermediation channel to regional output. Results show that BPR intermediation (X₁) and commercial-bank intermediation (X₂) significantly and positively affect MSME production (H4 and H5 supported; p < 0.05), whereas fintech P2P lending (X₃) does not (H6 rejected; p = 0.161). Fintech P2P lending positively influences per-capita consumption (H3 supported at the 90% level; p ≈ 0.078), while BPR and commercial-bank intermediation have no significant effect (H1 and H2 rejected). MSME production strongly enhances GRDP (H7 supported; p < 0.01), and consumption is positively associated with GRDP at the 90% level (H8 supported; p ≈ 0.085). Direct paths from intermediation to GRDP (H9–H11) are not significant, supporting a mediated mechanism: BPRs and commercial banks primarily operate through MSME production, while fintech channels influence GRDP via consumption. Model fit is acceptable (SRMR = 0.091), although some covariance-fit indices are weak (discussed in the manuscript), so inference relies on bootstrapped path estimates and mediation diagnostics. Policy implications suggest regulators should reinforce BPR and commercial-bank support for MSME financing, while guiding fintech to expand safe consumer access that enhances household purchasing power without introducing systemic risks.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".