Are ICT investments paying off in Africa? An analysis of total factor productivity in six West African countries from 1995 to 2002
Bibliographic record
Abstract
In the past two decades, we have seen increasing debate about information and communication technology (ICT) as an engine of growth that could lift developing nations out of poverty. Many African nations have implemented market liberalization and invested huge sums of money into their ICT sectors. But few studies have been conducted to assess the effectiveness of these investments. Demonstrating ICT sector performance is especially important because of challenges of the development of ICT policy and the United Nations agencies inability to state firmly if there are benefits to these investments. In this article, we investigated the total factor productivity (TFP) of the ICT sectors in six West African countries from 1995 to 2002. While the findings demonstrate positive growth in TFP, there is cause for concern. TFP growth in the ICT sector has been declining, and these countries are not yet able to take advantage of scale efficiencies. Careful attention must be given to future ICT investment strategies and performance management of existing ICT infrastructure if continued growth is to be achieved. © 2008 Wiley Periodicals, Inc.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".