Is Promoting Mobile Money Usage Consistent With Restricting Access to Phone Communication?: An Analysis of Direct and Indirect Network Effects in Mobile Money Adoption in Burkina Faso
Bibliographic record
Abstract
ABSTRACT To increase financial inclusion in Africa, many governments are promoting mobile money usage. But at the same time, despite efforts, relatively high costs characterize traditional mobile phone communication (calls, text messages, internet access, social media, etc.). Also, non‐price barriers like restricted access to internet or low national coverage rate of mobile communication signal may have led many people to have difficult access to traditional mobile phone communication. In this paper, we investigate the role of indirect (traditional phone communication network) and direct (mobile money network) network effects on mobile money adoption in Burkina Faso. We use FinScope data and a recursive multivariate probit model to find that, in Burkina Faso, mobile money services benefit more from the indirect network effects than the direct network effects. In other words, it is inconsistent to impose large taxes and fees on phone communication or to limit access to internet, messaging apps and social media, while promoting mobile money adoption.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".