Augmented Human Capital: A Unified Theory and LLM-Based Measurement Framework for Cognitive Factor Decomposition in AI-Augmented Economies
TLDR
This paper decomposes human capital, showing AI substitutes routine tasks but complements augmentable cognitive work, with formal employment crucial for AI benefits.
Key contributions
- Proposes a new human capital decomposition into physical, routine-cognitive, and augmentable-cognitive components.
- Develops a production function where AI substitutes routine cognitive work but complements augmentable cognitive work.
- Finds formal workers gain from AI augmentation (+0.051 wage return), while informal workers cannot capture these rents.
- Highlights labor market institutions, not technology access, as the key constraint for AI complementarity in the Global South.
Why it matters
This paper offers a novel framework for understanding AI's asymmetric impact on human capital. It provides the first developing-country evidence, showing that labor market institutions, not technology access, are the binding constraint for capturing AI augmentation benefits. This is crucial for policy in the Global South.
Original Abstract
This paper proposes a decomposition of human capital into three orthogonal components -- physical-manual (H^P), routine-cognitive (H^C), and augmentable-cognitive (H^A) -- and develops a production function in which AI capital interacts asymmetrically with these components: substituting for routine cognitive work while complementing augmentable cognitive work through an amplification function phi(D). I derive a corrected Mincerian wage equation and show that the standard specification is misspecified in AI-augmented economies. Using LLM-generated measures of occupational augmentability for 18,796 O*NET task statements mapped to 440 Colombian occupations, merged with household survey microdata (N = 105,517 workers), I estimate the augmented Mincer equation. The wage return to H^A increases with AI adoption in the formal sector (beta_2 = +0.051, p < 0.001), while informal workers cannot capture augmentation rents (beta_2 = -0.044). A triple interaction confirms formality as the binding mechanism (beta_{AHC x D x Formal} = +0.272, p < 0.001). The augmentation premium is strongest for experienced workers (ages 46-65) and in health and education sectors. These results provide the first developing-country evidence of cognitive factor decomposition in AI-augmented labor markets and demonstrate that the binding constraint on human-AI complementarity in the Global South is not technology access but labor market institutions.
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