Who Benefits from Salary Prediction Tools? A Simulation Study of Worker Power and Wage Inequality in AI-Mediated Salary Negotiations

Who Controls the Gains from Workplace AI? Time, Targets and Pay

Workplace AI changes the use of paid time before it necessarily changes workers' pay. This commentary synthesizes recent experiments and worker surveys alongside earlier implementation cases to explain how efficiency gains reach employees. Three allocation patterns emerge. In a Danish study, 85% of chatbot users report...

Collective Bargaining and the Wage Structure

Welfare Implications of Output-Based Pay among Vulnerable Workers in Indonesia

Payment by output rather than by time has expanded in Indonesia as a source of income amid limited formal job creation. This study asks whether workers paid solely by output or service fee report different earnings from wage-employed workers, and which groups show the largest differentials. Using Sakernas August 2024,...

Statistical Discrimination Revisited: Explaining the Early Gender Wage Gap

When Classification Fails, Compensation Follows: An AI-Assisted Job Classification Framework for Large Public Sector Employers

This case study describes a classification reform at a large urban U.S. public school district of 11,480 employees and $724 million in payroll. An internal pay equity review found pay gaps between demographic groups that job content and tenure did not explain. The gaps came from how jobs had been assigned to titles and...

Asymmetric Information in Wage Contracts: Experimental Evidence and Welfare Implications

Compared to self-employment or performance-based pay, fixed wages reduce earnings uncertainty but are prone to market distortions through moral hazard and adverse selection. Using a model of wage contracts under asymmetric information, I show that these distortions map to marginal treatment response functions in a marg...