Regulatory innovations in Tanzania: the role of administrative capabilities and regulatory governance
Bibliographic record
Abstract
Purpose The purpose of this paper is to show that, while in many low income countries inefficient regulatory regimes have been blamed for impeding ICT market development, Tanzania constitutes a remarkable exception. This study aims to identify the organizational and contextual factors that have enabled the Tanzanian Communications Regulatory Agency (TCRA) to implement innovative regulations, including a fully converged licensing framework as the first country on the continent, and how subsequently these regulations have influenced market development. Design/methodology/approach The analysis is based on case study data gathered through 20 face‐to‐face interviews in 2006 as well as secondary data gathered from government documents, news reports and company web sites. Findings The research finds that the market developments and regulatory innovations were due in part to Tanzania's Communications Regulatory Authority (TCRA)'s high level of autonomy, afforded by independent funding mechanisms and lack of capacity of the Ministry, which pressed the regulator to play a greater role in policy making than is found in other countries. Further, TCRA's significant internal focus on capacity building has also enabled strong regulatory governance. Practical implications The results provide further evidence of the role that institutional endowments and regulatory governance play in fostering policy reform. Originality/value The research examines regulatory innovations in a region typically associated with regulatory inefficiencies. It identifies institutional factors and subsequently shows how in a very low income country they may be conducive to effective regulatory governance and market development.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 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".