Impact of Monetary Policy on Financial Inclusion in Emerging Markets
Bibliographic record
Abstract
The study investigates the impact of monetary policy on the level of financial inclusion in the big-five emerging market countries from 2004 to 2020. Several indicators of financial inclusion and the central bank interest rate were used in the analysis. It was found that the monetary policy rate has a mixed effect on financial inclusion, and the effect depends on the dimension of financial inclusion examined. Specifically, a high monetary policy rate has a significant negative impact on financial inclusion through a reduction in the number of depositors in commercial banks. A high monetary policy rate also has a significant positive impact on financial inclusion through greater bank branch expansion. The policy implication is that both contractionary and expansionary monetary policies lead to positive improvements in specific indicators of financial inclusion, because increase in interest rate leads to bank branch expansion which is beneficial for financial inclusion and decrease in interest rate leads to increase in the number of depositors in commercial banks which is also beneficial for financial inclusion. It was also found that the rising monetary policy rate has a negative effect on all indicators of financial inclusion in the post-financial crisis period. Overall, the effect of monetary policy on financial inclusion seem to depend on the monetary policy tool used by the monetary authority and the dimension of financial inclusion examined. The monetary authorities should pay attention to how their monetary policy choices might affect the level of financial inclusion and reduce the benefits that society gains from 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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".