Trade Openness-Government Size Nexus: Compensation Hypothesis Considered for Nigeria
Bibliographic record
Abstract
Over the years, substantial theoretical and empirical studies have been carried out on the trade openness-government size nexus. While a strand of the literature reported positive linkage, the other suggests otherwise. This study contributes to the debate by examining this relationship for Nigeria using the bounds testing approach to cointegration within an ARDL framework proposed by Pesaran et al. (2001). Empirical evidence reveals that government size measured by percentage share of total government expenditure in GDP and share (percent) of recurrent expenditure in GDP significantly affects trade openness in the long run but percentage share of capital expenditure in GDP as a measure of government size does not impact on trade openness in the long run. The results of the standard causality test corroborate these findings. However, the three measures of government size considered significantly affect trade openness in the short run. The major implication for our study therefore is that compensation hypothesis holds for Nigeria. Thus, the government need to continue to expand its expenditure in order to cushion the effect of increase in risk caused by rising trade openness.
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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 | 0.001 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".