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<p>Despite extensive research, a consensus remains elusive regarding the optimal method for measuring the effects of technological change and innovation on employment. This study introduces a Technological Change Composite Indicator (TCI), constructed using Principal Component Analysis (PCA) to synthesize seven firm-level innovation metrics. This methodology mitigates issues associated with multicollinearity in regression analyses involving correlated variables. The proposed TCI serves as a proxy for technological change to examine its association with employment in manufacturing sectors across European Union countries. Applying the TCI to a pooled cross‑section of ten European countries and seventeen manufacturing sectors (170 observations) with country fixed effects and a one‑year time lag, we find that a one‑unit increase in the TCI corresponds to a 0.58% higher employment level. The association is positive and statistically significant, indicating that a multidimensional measure of technological change outperforms traditional single proxies such as R&amp;D expenditure or patent counts. The TCI provides policymakers and industry stakeholders with a novel framework for assessing how a broad portfolio of innovation activities—including machinery acquisitions, external knowledge, intellectual property rights, and both product‑ and process‑oriented efforts—relates to manufacturing employment. By moving beyond narrow indicators, our approach offers a more reliable empirical basis for understanding the employment implications of technological change, including emerging technologies such as AI.</p>

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The impact of technological change on employment: a composite indicator approach

  • 期刊论文

  • 发表日期：

    2026年7月24日

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  • Rahim Taghizadeh,
  • Salar Babazadeh Behestani,
  • G. Reza Arabsheibani

摘要

Despite extensive research, a consensus remains elusive regarding the optimal method for measuring the effects of technological change and innovation on employment. This study introduces a Technological Change Composite Indicator (TCI), constructed using Principal Component Analysis (PCA) to synthesize seven firm-level innovation metrics. This methodology mitigates issues associated with multicollinearity in regression analyses involving correlated variables. The proposed TCI serves as a proxy for technological change to examine its association with employment in manufacturing sectors across European Union countries. Applying the TCI to a pooled cross‑section of ten European countries and seventeen manufacturing sectors (170 observations) with country fixed effects and a one‑year time lag, we find that a one‑unit increase in the TCI corresponds to a 0.58% higher employment level. The association is positive and statistically significant, indicating that a multidimensional measure of technological change outperforms traditional single proxies such as R&D expenditure or patent counts. The TCI provides policymakers and industry stakeholders with a novel framework for assessing how a broad portfolio of innovation activities—including machinery acquisitions, external knowledge, intellectual property rights, and both product‑ and process‑oriented efforts—relates to manufacturing employment. By moving beyond narrow indicators, our approach offers a more reliable empirical basis for understanding the employment implications of technological change, including emerging technologies such as AI.