Growth Vis-à-Vis Development: Are the Countries Converging or Diverging Over Time?
Bibliographic record
Abstract
The persistently large income gap between the developed countries (DCs) of the North and relatively less developed and developing countries (LDDCs) of the South is one of the most notable features of the international community over the last few decades. Different research works in this field have indicated that the average annual growth rate of per capita income (PCI) in LDDCs has been faster compared to that in DCs particularly since early 1990s indicating a sign of convergence in the growth process. However, the absolute gap between the DCs and LDDCs in terms of per capita Gross national product (GNP) has widened over years. In this backdrop, this chapter is an attempt to enquire into the dynamics of the gap between the developed, developing and less developed parts of the world over the period from 1990 to 2023. The beta-convergence analysis, using dynamic panel data regression, leaves emphatic evidence of conditional convergence, rather than the absolute, implying a case of typical club convergence, where the countries sharing similar conditions in health, education and prevalence of absolute poverty tend to converge in terms of PCI. This chapter essentially points out that a mere convergence in the growth rate does not necessarily translate into the narrowing of income and development gap, based on the selected 150 countries across the globe, classified with respect to income level, high, middle and low and four broad regions, namely, Latin American Caribbean (LAC), Sub-Saharan Africa (SSA), Middle East and North Africa (MNA) and East Asia Pacific (EAP), epitomising the developing world.
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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 | 0.004 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".