Democratic regulation of AI in the workplace

B-Tier
Journal: Games and Economic Behavior
Year: 2025
Volume: 152
Issue: C
Pages: 113-132

Authors (2)

Roy, Jaideep (University of Bath) Saha, Bibhas (not in RePEc)

Score contribution per author:

1.005 = (α=2.01 / 2 authors) × 1.0x B-tier

α: calibrated so average coauthorship-adjusted count equals average raw count

Abstract

When artificial intelligence (AI) displaces lower-skilled workers with higher intensity, electoral democracies may slow down automation in fear of unemployment and voter resentment. Using a Downsian model of elections where parties promise to limit automation and redistribute automation surplus, we show that when automation is highly productive democracies implement maximum automation, making all workers vulnerable to redundancy and distribute the entire surplus among the working population. Majority of the workers are gainers in the sense that their expected earnings exceed their (pre-automation) wage. When the automation surplus is low, democracies restrict automation and protect the high-skilled workers (including the median-skilled worker) but redistribute nothing to the vulnerable workers. Here, because of no compensation for redundancy all vulnerable workers become losers as their expected earnings fall below their basic wage. For highly productive automation, democracies achieve the first best worker welfare but otherwise may over- or under-provide automation.

Technical Details

RePEc Handle
repec:eee:gamebe:v:152:y:2025:i:c:p:113-132
Journal Field
Theory
Author Count
2
Added to Database
2026-01-29