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AI and the Future of Work in an Aging Economy

Pizzinelli, Tavares

2025Pension Research Council Working Paper
Exposure / measurement
AI (General)Senior / older workersAugmentation vs. substitutionTraining / upskillingOccupational mobilityGeneral automation
DOI: 10.2139/ssrn.5345347
Summary

Pizzinelli and Tavares use occupation-level AI exposure and complementarity measures combined with US labor force surveys to examine how older workers (55+) may be affected by AI-driven structural transformation compared to younger age groups.

Main Finding

Older workers (55+) have lower labor market fluidity but are currently employed in AI-complementary occupations at higher rates than younger workers (especially among college-educated), suggesting potential opportunities if they acquire necessary AI literacy skills, though historical automation episodes show differential cohort impacts with prime-age workers facing larger disruptions.

Primary Datasets

Cross-country labor data on occupational mobility by age

Secondary Datasets

Felten et al. (2021)

AI-focused

Felten et al. (2021) AI Occupational Exposure Index; Pizzinelli et al. (2023) AI complementarity index

Key Methods
Descriptive cross-tabulation by age group using AI exposure/complementarity measures; historical cohort analysis of employment shifts during automation waves; life-cycle labor market fluidity indicators
Sample Period
2010-2019 (main analysis); 1970-2019 (historical cohort analysis); 2023-2024 (telework/health characteristics)
Geographic Coverage
International
Sample Size
US working-age population across multiple Census Bureau surveys; specific N not reported
Level of Analysis
Occupation, Individual
Occupation Classification
US Census 4-digit; SOC 2010; SOC 2018
Industry Classification
NAICS 4-digit
Notes
Wharton PRC WP 2025-14; older workers have lower fluidity but larger share in AI-complementary occupations [Claude classification]: Working paper from Wharton Pension Research Council (PRC WP2025-14). Uses existing AI exposure measures (Felten et al. 2021) and complementarity index (Pizzinelli et al. 2023) rather than constructing new measures, but applies them to age-differentiated analysis. Historical case studies examine routine-biased automation (1980-2000) and computerization (2000-2019) effects on different birth cohorts. Discusses AI literacy requirements and policy implications for older workers. Focus is on male workers with female results in appendix. [Claude classification]: Working paper from Wharton Pension Research Council (PRC WP2025-14). Uses existing AI exposure measures (Felten et al. 2021) and complementarity index (Pizzinelli et al. 2023) rather than constructing new measures, but applies them to age-differentiated analysis. Historical case studies examine routine-biased automation (1980-2000) and computerization (2000-2019) effects on different birth cohorts. Discusses AI literacy requirements and policy implications for older workers. Focus is on male workers with female results in appendix. [Claude classification]: Working paper from Wharton Pension Research Council (PRC WP2025-14). Uses existing AI exposure measures (Felten et al. 2021) and complementarity index (Pizzinelli et al. 2023) rather than constructing new measures, but applies them to age-differentiated analysis. Historical case studies examine routine-biased automation (1980-2000) and computerization (2000-2019) effects on different birth cohorts. Discusses AI literacy requirements and policy implications for older workers. Focus is on male workers with female results in appendix. [Claude classification]: Working paper from Wharton Pension Research Council (PRC WP2025-14). Uses existing AI exposure measures (Felten et al. 2021) and complementarity index (Pizzinelli et al. 2023) rather than constructing new measures, but applies them to age-differentiated analysis. Historical case studies examine routine-biased automation (1980-2000) and computerization (2000-2019) effects on different birth cohorts. Discusses AI literacy requirements and policy implications for older workers. Focus is on male workers with female results in appendix. [Claude classification]: Working paper from Wharton Pension Research Council (PRC WP2025-14). Uses existing AI exposure measures (Felten et al. 2021) and complementarity index (Pizzinelli et al. 2023) rather than constructing new measures, but applies them to age-differentiated analysis. Historical case studies examine routine-biased automation (1980-2000) and computerization (2000-2019) effects on different birth cohorts. Discusses AI literacy requirements and policy implications for older workers. Focus is on male workers with female results in appendix. [Claude classification]: Working paper from Wharton Pension Research Council (PRC WP2025-14). Uses existing AI exposure measures (Felten et al. 2021) and complementarity index (Pizzinelli et al. 2023) rather than constructing new measures, but applies them to age-differentiated analysis. Historical case studies examine routine-biased automation (1980-2000) and computerization (2000-2019) effects on different birth cohorts. Discusses AI literacy requirements and policy implications for older workers. Focus is on male workers with female results in appendix. [Claude classification]: Working paper from Wharton Pension Research Council (PRC WP2025-14). Uses existing AI exposure measures (Felten et al. 2021) and complementarity index (Pizzinelli et al. 2023) rather than constructing new measures, but applies them to age-differentiated analysis. Historical case studies examine routine-biased automation (1980-2000) and computerization (2000-2019) effects on different birth cohorts. Discusses AI literacy requirements and policy implications for older workers. Focus is on male workers with female results in appendix. [Claude classification]: Working paper from Wharton Pension Research Council (PRC WP2025-14). Uses existing AI exposure measures (Felten et al. 2021) and complementarity index (Pizzinelli et al. 2023) rather than constructing new measures, but applies them to age-differentiated analysis. Historical case studies examine routine-biased automation (1980-2000) and computerization (2000-2019) effects on different birth cohorts. Discusses AI literacy requirements and policy implications for older workers. Focus is on male workers with female results in appendix. [Claude classification]: Working paper from Wharton Pension Research Council (PRC WP2025-14). Uses existing AI exposure measures (Felten et al. 2021) and complementarity index (Pizzinelli et al. 2023) rather than constructing new measures, but applies them to age-differentiated analysis. Historical case studies examine routine-biased automation (1980-2000) and computerization (2000-2019) effects on different birth cohorts. Discusses AI literacy requirements and policy implications for older workers. Focus is on male workers with female results in appendix. [Claude classification]: Working paper from Wharton Pension Research Council (PRC WP2025-14). Uses existing AI exposure measures (Felten et al. 2021) and complementarity index (Pizzinelli et al. 2023) rather than constructing new measures, but applies them to age-differentiated analysis. Historical case studies examine routine-biased automation (1980-2000) and computerization (2000-2019) effects on different birth cohorts. Discusses AI literacy requirements and policy implications for older workers. Focus is on male workers with female results in appendix. [Claude classification]: Working paper from Wharton Pension Research Council (PRC WP2025-14). Uses existing AI exposure measures (Felten et al. 2021) and complementarity index (Pizzinelli et al. 2023) rather than constructing new measures, but applies them to age-differentiated analysis. Historical case studies examine routine-biased automation (1980-2000) and computerization (2000-2019) effects on different birth cohorts. Discusses AI literacy requirements and policy implications for older workers. Focus is on male workers with female results in appendix. [Claude classification]: Working paper from Wharton Pension Research Council (PRC WP2025-14). Uses existing AI exposure measures (Felten et al. 2021) and complementarity index (Pizzinelli et al. 2023) rather than constructing new measures, but applies them to age-differentiated analysis. Historical case studies examine routine-biased automation (1980-2000) and computerization (2000-2019) effects on different birth cohorts. Discusses AI literacy requirements and policy implications for older workers. Focus is on male workers with female results in appendix. [Claude classification]: Working paper from Wharton Pension Research Council (PRC WP2025-14). Uses existing AI exposure measures (Felten et al. 2021) and complementarity index (Pizzinelli et al. 2023) rather than constructing new measures, but applies them to age-differentiated analysis. Historical case studies examine routine-biased automation (1980-2000) and computerization (2000-2019) effects on different birth cohorts. Discusses AI literacy requirements and policy implications for older workers. Focus is on male workers with female results in appendix. [Claude classification]: Working paper from Wharton Pension Research Council (PRC WP2025-14). Uses existing AI exposure measures (Felten et al. 2021) and complementarity index (Pizzinelli et al. 2023) rather than constructing new measures, but applies them to age-differentiated analysis. Historical case studies examine routine-biased automation (1980-2000) and computerization (2000-2019) effects on different birth cohorts. Discusses AI literacy requirements and policy implications for older workers. Focus is on male workers with female results in appendix. [Claude classification]: Working paper from Wharton Pension Research Council (PRC WP2025-14). Uses existing AI exposure measures (Felten et al. 2021) and complementarity index (Pizzinelli et al. 2023) rather than constructing new measures, but applies them to age-differentiated analysis. Historical case studies examine routine-biased automation (1980-2000) and computerization (2000-2019) effects on different birth cohorts. Discusses AI literacy requirements and policy implications for older workers. Focus is on male workers with female results in appendix. [Claude classification]: Working paper from Wharton Pension Research Council (PRC WP2025-14). Uses existing AI exposure measures (Felten et al. 2021) and complementarity index (Pizzinelli et al. 2023) rather than constructing new measures, but applies them to age-differentiated analysis. Historical case studies examine routine-biased automation (1980-2000) and computerization (2000-2019) effects on different birth cohorts. Discusses AI literacy requirements and policy implications for older workers. Focus is on male workers with female results in appendix. [Claude classification]: Working paper from Wharton Pension Research Council (PRC WP2025-14). Uses existing AI exposure measures (Felten et al. 2021) and complementarity index (Pizzinelli et al. 2023) rather than constructing new measures, but applies them to age-differentiated analysis. Historical case studies examine routine-biased automation (1980-2000) and computerization (2000-2019) effects on different birth cohorts. Discusses AI literacy requirements and policy implications for older workers. Focus is on male workers with female results in appendix. [Claude classification]: Working paper from Wharton Pension Research Council (PRC WP2025-14). Uses existing AI exposure measures (Felten et al. 2021) and complementarity index (Pizzinelli et al. 2023) rather than constructing new measures, but applies them to age-differentiated analysis. Historical case studies examine routine-biased automation (1980-2000) and computerization (2000-2019) effects on different birth cohorts. Discusses AI literacy requirements and policy implications for older workers. Focus is on male workers with female results in appendix. [Claude classification]: Working paper from Wharton Pension Research Council (PRC WP2025-14). Uses existing AI exposure measures (Felten et al. 2021) and complementarity index (Pizzinelli et al. 2023) rather than constructing new measures, but applies them to age-differentiated analysis. Historical case studies examine routine-biased automation (1980-2000) and computerization (2000-2019) effects on different birth cohorts. Discusses AI literacy requirements and policy implications for older workers. Focus is on male workers with female results in appendix. [Claude classification]: Working paper from Wharton Pension Research Council (PRC WP2025-14). Uses existing AI exposure measures (Felten et al. 2021) and complementarity index (Pizzinelli et al. 2023) rather than constructing new measures, but applies them to age-differentiated analysis. Historical case studies examine routine-biased automation (1980-2000) and computerization (2000-2019) effects on different birth cohorts. Discusses AI literacy requirements and policy implications for older workers. Focus is on male workers with female results in appendix.