Absolute and Conditional Convergence in Both Zones of <scp>C</scp>yprus: Statistical Convergence and Insitutional Divergence
Bibliographic record
Abstract
Abstract This paper implements a time series econometric model to determine the timing of full convergence of incomes and output per capita and total factor productivity in the North and South of Cyprus, regardless of whether there is a political settlement or not. A significant dimension of the paper is its emphasis on institutional convergence, going beyond econometric or statistical convergence. Our results reveal that North Cyprus needs 17 years to catch up to full per capita income convergence, 16 years for per capita output convergence and 17 years for full total factor productivity (technological) convergence. The time‐series findings demonstrate that statistical convergence is occurring quite rapidly as the North is catching up to the average income and productivity levels of the South, which may confirm evidence of unconditional (beta) or absolute convergence, but there are significant differences between North and South in savings, tastes, population growth and technology. Most significantly, there are institutional differences highlighted in the study with a Two‐sector model of gate‐keeping and rent‐seeking which validates the premises of conditional convergence. Put differently, there are strong forces of divergence hidden behind our statistical findings.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".