Does income inequality influence the role of a sharing economy in promoting sustainable economic growth? Fresh evidence from emerging markets
Bibliographic record
Abstract
The impact of the sharing economy has become increasingly prominent in facilitating sustainable economic growth. The current study examined this relationship in the context of emerging markets. It addressed the influence of income inequality on restricting the expected benefits from activities associated with the sharing of assets or services. The study employed panel data from 20 developing countries across Africa and Asia from 2001 to 2020 and used dynamic models to mitigate the impact of endogeneity. The study utilised a proxy indicator for sharing economies developed in the literature, as well as three different measures for income inequality, in order to ensure robust findings. The study employed the generalised method of moments (GMM) as its primary methodology. The GMM results confirmed previous findings from developed countries in which the sharing economy tended to promote the sustainable growth of the economy. Income inequality was observed to have a negative relationship with sustainable economic growth, however, and this indicated that it hampered the ability of the sharing economy to stimulate sustainable growth. Interestingly, when the analysis included interaction terms to capture the moderating impact of income inequality there was more consistency with previous research. The interaction term had a negative coefficient, indicating that income inequality tended to act as an impediment in developing countries to the full capturing of the benefits of peer-to-peer transactions. These findings provided useful insights into collaborative consumption and the peer economy, given that the aim of the sharing of resources would be to capture rent from underused assets. The study suggested that the development of efficient and effective platforms would allow developing countries to capture the benefits of the sharing economy.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".