Institutional Quality, Digital Readiness, and Economic Growth: Constructing and Testing a Government Project Maturity Index
Bibliographic record
Abstract
This paper investigates the impact of government project management maturity (GPMI) on gdp per capita growth across 159 countries using panel data for 2016, 2018, 2020, 2022, and 2024. The study constructs a novel gpmi by combining six worldwide governance indicators and the e-government development index to capture institutional quality and digital readiness essential for effective public project management. The study tests the hypotheses that higher gpmi contributes to economic growth, with a particular focus on the potential delayed effects of improvements in project management maturity on economic performance. Panel fixed effects regression models were employed to analyze the relationship while controlling for inflation, trade openness, urban population share, and the log of gdp per capita. The results indicate that gpmi has a positive and statistically significant effect on gdp per capita growth when measured with a time lag, confirming that the benefits of institutional and project management reforms require time to materialize. Additionally, inflation was found to negatively impact growth, while trade openness showed a positive association. Urban population share exhibited a significant negative relationship with gdp growth, highlighting the potential infrastructure pressures associated with urbanization. These findings contribute to the literature on public sector management and economic development by emphasizing the importance of investing in government project management capacity as part of long-term development strategies.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| 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".