Artificial Intelligence Exposure, Perceived Job Replaceability, and Perceived Income Change: The Moderating Role of Task Codifiability—Evidence from the China General Social Survey

This study examines how artificial intelligence (AI) exposure is associated with Chinese residents’ perceived income change and how this association varies with Perceived Job Replaceability. Using a nationally representative sample of 6247 employed workers from the 2021 China General Social Survey matched with industry-level robot penetration data, we estimate ordered probit models and two-stage residual-inclusion control-function checks within an ordered-response framework. Three findings emerge. First, objective AI exposure is associated with a 4.2-percentage-point lower probability of reporting higher household income than last year for a one-standard-deviation increase in robot density, based on predicted-probability contrasts rather than raw ordered-probit coefficients. Second, workers reporting low Perceived Job Replaceability are 15.7 percentage points more likely to report higher household income than last year on the same probability scale. Third, task codifiability moderates this relationship: marginal-effect contrasts show that low Perceived Job Replaceability is associated with a 22.9 percentage-point increase in the probability of reporting higher household income than last year in low-codifiability occupations, compared with only 4.7 percentage points in high-codifiability occupations, indicating a polarization pattern in perceived income change outcomes. Heterogeneity analyses further show that medium-skill routine workers face the strongest negative exposure associations, whereas high-skill workers in low-codifiability occupations show the strongest positive low-replaceability contrast. Overall, the findings clarify how AI exposure, worker perceptions, and task structure are jointly associated with perceived income change in China and provide evidence relevant to more inclusive technological adjustment policies.