Role of Remittance on Sustainable Economic Development in Developing and Emerging Economies: New Insights from Panel Cross-Sectional Augmented Autoregressive Distributed Lag Approach
Bibliographic record
Abstract
In this study, we aim to investigate the effects of remittance on sustainable economic development in 52 developing and emerging economies from 1996 to 2021. The study uses other variables such as real GDP per capita, total natural resource rents, globalization, and foreign direct investment. To achieve the mentioned objective, we apply a series of second-generation panel estimation approaches. These include CIPS unit root, Westerlund cointegration, cross-sectional augmented autoregressive distributed lag (CS-ARDL), and robustness using augmented mean group (AMG) and common correlated mean group (CCEMG). These methods are useful provided they are robust towards cross-country dependencies, slope heterogeneity, endogeneity, and serial correlation, which are disregarded in the conventional panel estimations. The empirical findings indicate that remittance accelerates sustainable economic development. Additionally, real GDP per capita and globalization also positively contribute towards sustainable economic development. However, total resource rents deteriorate sustainable economic development. This study offers key policy implications based on the empirical findings for the developing and emerging economies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".