The Interaction Effect of Financial Innovation and the Transmission Channels on Money Demand in Uganda
Bibliographic record
Abstract
The current study set out to examine the direct as well as the indirect effect of technological advances and diversification of the financial sector on money demand in Uganda using annual data from 1986 to 2017. The results derived on the basis of the ARDL framework provide evidence in confirmation of the significant role played by financial innovation in the demand for real narrow money balances both directly and indirectly via the real income as well as the inflation rate channels in the shortrun and longrun. The association appears to increase (decrease) once inflation (real income) decreases (increases). On the contrary, we find no evidence that the exchange rate and the interest rate channels matter in the financial-innovation-money-demand linkage although their independent effect is significantly indismissible. Surprisingly, besides the exchange rate and inflation rate, data does not allow conclusion of any significant role of financial innovation in real broad money balances. Finally, the money demand function is found stable over the study period. Overall, supportive policies that are pro-advancement in the creation and popularizing of the new financial instruments as well as new financial technologies, institutions and markets are strongly recommended for purposes of enhancing pro-growth financial innovations. Policy makers ought to give more attention to the income and inflation rate transmission channels if financial innovations are to be a benefit rather than a risk to money demand stability. The adoption of the inflation lite monetary framework was therefore a right step in the right direction.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".