ECOBAS Research Seminar: Raquel Sebastián Lago (UCM)
Abstract
Technological change fuels economic growth, but its impact on wage inequality remains contested. This study presents a unified empirical framework that isolates the effects of new technologies such as automation and AI on the entire wage distribution. The authors develop a continuous and task-sensitive automation index and propose a distributional counterfactual based method. Applying the approach to Spanish micro-data for 2000-2019 and instrumenting technology variables, they find automation to be a key driver of inequality: without task displacement the Gini coefficient would be 21.5% lower and significant wage shares would shift from the top 10% towards middle and bottom groups. Automation is found to barely affect the gender gap in the period studied, yet to widen the education premium. Like automation, AI exposure increases inequality, although the mechanisms to impact wages differ: automation tends to negatively impact wages in the middle of the distribution, while AI tends to increase wages at the top. Trade, offshorability, educational attainment, employment rates and mark-ups play secondary, period-specific roles. The results can inform policies on skill formation and inclusive innovation.
ECOBAS Junior Research Seminar: Victor Krakovich, Universidade de Vigo
While human capital is widely recognized as a key driver of firm performance, existing research remains fragmented across disciplines with differing conceptual frameworks and valuation logics.
They address this gap by introducing an investment cycle framework that distinguishes three stages of human capital valuation: ex ante (signals such as education and skills), investment (training and compensation costs), and ex post (realized outcomes such as wages, productivity, and firm value).
Using a systematically constructed dataset of articles from Scopus, and applying LLM-based classification, we map the literature across stages and levels of analysis. The results highlight important research gaps and provide a coherent foundation for future empirical work.
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