Evidence map›Paper›PMID 39897810›Full record

ArticleHeliyon2025

Analyzing wealth distribution effects of artificial intelligence: A dynamic stochastic general equilibrium approach.

Fang Liu, Chen Liang

Abstract read
In one paragraph

Article in Heliyon, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Fang LiuEconomic School of Changzhou University, China.
Chen LiangFinancial Technology School of Shenzhen University, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This study explores the often-overlooked influence of artificial intelligence (AI) on wealth distribution. Using a continuous-time heterogeneous agent dynamic general equilibrium model, we investigate AI's impact on production technology as a form of biased technological progress. Our findings highlight a temporal dichotomy in AI's effects on wealth inequality: in the short term, AI exacerbates disparities in wealth distribution, while the long-term outcomes depend on the extent of AI's influence across different technological domains. The nuanced nature of AI-driven technological progress leads to distinct consequences for the rate of return on capital-showing short-term increases across all forms of technological progress, but with varying long-term effects. Based on these results, we propose policy recommendations for China to leverage AI for economic growth while mitigating inequality. This research provides a comprehensive analysis of AI's role in wealth distribution, offering valuable insights for both academic inquiry and policy formulation.

Indexed as

Artificial intelligenceHeterogeneous agent dynamic general equilibrium modelWealth distribution

Identifiers

PMID39897810
PMCPMC11786846

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.