Institutional development in an information-driven economy: can ICTs enhance economic growth for low- and lower middle-income countries?
Bibliographic record
Abstract
The information and communication technology (ICT) revolution has brought positive spill-over effects on institutions and economies across the globe, but it has also increased the information gaps between countries. A key characteristic that may explain these widening gaps is the deepening endogenous relationships between ICT infrastructure, institutions of governance, and economic growth in many developing countries. Thus far, the links between these variables have not been discernible in developing economies, so few studies have explored them. In this paper, we investigate the possible Granger causal relationships among institutional quality, economic growth, and ICT infrastructure development for a sample of developing countries for the period from 2005 to 2019. The application of a vector error-correction model reveals strong inter-relationships between all the variables in the short run. In the long run, institutional quality and ICT infrastructure development stimulate economic growth. These complex relationships are explored and lessons are drawn for policymakers.RESEARCH HIGHLIGHTS We assess interactions between institutional quality and ICT infrastructure as well as economic growth.We deploy a panel Granger causality test for low- and lower middle-income countries from 2005 to 2019.We show that there is Granger causality between the variables in the short and the long term.For each case and specification, there is support for the hypothesis that ICT infrastructure and institutional quality both Granger-cause growth in the economy.In the short run, we note a feedback relationship between institutional quality and economic growth. Other short-run results are more varied, based on the particulars proxies for institutional quality and ICT infrastructure.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.000 | 0.003 |
| 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".