Does Information and Communication Technology Lead to the Well-Being of Nations? A Country-Level Empirical Investigation1
Bibliographic record
Abstract
This paper examines the role of information and communication technology (ICT) in enhancing the well-being of nations. Extending research on the role of ICT in the productivity of nations, we posit that the effects of ICT may not be limited to productivity (e.g., GDP), and we argue that the use of ICT can also improve the well-being of a country by helping citizens to develop their social capital and achieve social equality, enabling access to health-related information and health services, providing education to disadvantaged communities, and facilitating commerce. Using a number of empirical specifications, specifically a fixed-effects model and an instrumental variable approach, our results show that the level of ICT use (number of fixed telephones, Internet, mobile phones) in a country predict a country’s well-being (despite accounting for GDP and several other control variables that also predict a country’s well-being). Furthermore, by using an exploratory method (biclustering) of identifying both country-specific and ICT-specific variables simultaneously, we identify clusters of countries with similar patterns in terms of their use of ICT, and we show that not all countries increase their level of well-being by using ICT in the same manner. Interestingly, we find that less developed countries increase their level of well-being with mobile phones primarily, while more developed countries increase their level of well-being with any ICT system. Contributions and implications for enhancing the well-being of nations with ICT are discussed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".