Does cultural characteristics moderate the effect of shadow economy on carbon emissions? Evidence from the Global South countries
Bibliographic record
Abstract
Climate change presents a serious challenge to economies in the Global South, where informal economic activities remain widespread. The shadow economy, while providing livelihoods, undermines environmental regulations and contributes to rising carbon emissions. Yet, the extent to which cultural characteristics shape this relationship remains underexplored. This study investigates how five of Hofstede’s cultural dimensions—power distance, individualism, masculinity, uncertainty avoidance, and long-term orientation—moderate the shadow economy–emissions nexus. Using panel data from 60 Global South countries between 1996 and 2018, we apply Prais–Winsten panel-corrected standard errors and feasible generalized least squares as baseline estimators, with a two-step system GMM for robustness against endogeneity. The results indicate that individualism, masculinity, and long-term orientation mitigate the emissions impact of informality, while high power distance and uncertainty avoidance amplify it. These findings highlight the role of cultural traits in shaping environmental outcomes and underscore the need for context-specific policy responses. Promoting cultural values that support responsibility, long-term planning, and compliance, while facilitating the transition of small enterprises into the formal economy, can reduce carbon intensity. The study provides new evidence on how cultural factors influence the environmental consequences of informality and offers insights for designing more effective climate and development strategies in the Global South.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 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".