{"id":113814,"date":"2024-01-22T18:41:13","date_gmt":"2024-01-22T17:41:13","guid":{"rendered":"https:\/\/www.wifo.ac.at\/publication\/113814\/"},"modified":"2025-07-08T17:37:21","modified_gmt":"2025-07-08T15:37:21","slug":"technological-change-and-learning-curves-in-the-context-of-the-transust-scan-modelling-network-transust-scan-working-paper","status":"publish","type":"publication","link":"https:\/\/www.wifo.ac.at\/en\/publication\/113814\/","title":{"rendered":"Technological Change and Learning Curves in the Context of the TranSust.Scan Modelling Network. TranSust.Scan Working Paper"},"content":{"rendered":"","protected":false},"featured_media":0,"template":"","class_list":["post-113814","publication","type-publication","status-publish","hentry"],"acf":{"subtitle":"","text":"The paper deals with the concept of learning curves against a broader background of modelling technological change. Based on a literature survey the paper addresses four approaches (endogenous growth theory, learning curves, innovation theory and diffusion theory) and attempts to highlight the links between them. The second part of the paper tackles the empirical implementation of technological change in TranSust.Scan models. Learning curves are the predominantly used approach in empirical modelling as they show two main benefits: they allow inclusion of different technologies for which specific learning rates are estimated. 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TranSust.Scan Working Paper<\\\/em><\\\/span>. WIFO. <a onclick=\\\"window.open(this.href, '_blank','noopener,noreferrer'); return false;\\\" href=\\\"https:\\\/\\\/www.wifo.ac.at\\\/wwa\\\/pubid\\\/38921\\\" class=\\\"link\\\"><span>https:\\\/\\\/www.wifo.ac.at\\\/wwa\\\/pubid\\\/38921<\\\/span><\\\/a><\\\/div>\",\"vancouver\":\"<div class=\\\"rendering rendering_researchoutput  rendering_researchoutput_vancouver rendering_bookanthology rendering_vancouver rendering_bookanthology_vancouver\\\"><span>Kettner C<\\\/span><span>, K\u00f6ppl A<\\\/span><span>, Schleicher S<\\\/span>. <span class=\\\"title\\\"><span>Technological Change and Learning Curves in the Context of the TranSust.Scan Modelling Network. TranSust.Scan Working Paper<\\\/span><\\\/span>. WIFO; 2008.<\\\/div>\",\"bibtex\":\"<div class=\\\"rendering rendering_researchoutput  rendering_researchoutput_bibtex rendering_bookanthology rendering_bibtex rendering_bookanthology_bibtex\\\"><div>@book{60dfbd58fc604722bb5dc9083d7ab429,<\\\/div><div>  title    = \\\"Technological Change and Learning Curves in the Context of the TranSust.Scan Modelling Network. TranSust.Scan Working Paper\\\",<\\\/div><div>  abstract = \\\"The paper deals with the concept of learning curves against a broader background of modelling technological change. Based on a literature survey the paper addresses four approaches (endogenous growth theory, learning curves, innovation theory and diffusion theory) and attempts to highlight the links between them. The second part of the paper tackles the empirical implementation of technological change in TranSust.Scan models. Learning curves are the predominantly used approach in empirical modelling as they show two main benefits: they allow inclusion of different technologies for which specific learning rates are estimated. Furthermore, evidence from ex-post observations and ex-ante estimation match closely.\\\",<\\\/div><div>  author   = \\\"Claudia Kettner and Angela K{\\\\\\\"o}ppl and Stefan Schleicher\\\",<\\\/div><div>  year     = \\\"2008\\\",<\\\/div><div>  month    = jun,<\\\/div><div>  language = \\\"English\\\",<\\\/div><p>}<\\\/p><\\\/div>\",\"ris\":\"<div class=\\\"rendering rendering_researchoutput  rendering_researchoutput_ris rendering_bookanthology rendering_ris rendering_bookanthology_ris\\\"><p>TY  - BOOK<\\\/p><p>T1  - Technological Change and Learning Curves in the Context of the TranSust.Scan Modelling Network. TranSust.Scan Working Paper<\\\/p><p>AU  - Kettner, Claudia<\\\/p><p>AU  - K\u00f6ppl, Angela<\\\/p><p>AU  - Schleicher, Stefan<\\\/p><p>PY  - 2008\\\/6<\\\/p><p>Y1  - 2008\\\/6<\\\/p><p>N2  - The paper deals with the concept of learning curves against a broader background of modelling technological change. Based on a literature survey the paper addresses four approaches (endogenous growth theory, learning curves, innovation theory and diffusion theory) and attempts to highlight the links between them. The second part of the paper tackles the empirical implementation of technological change in TranSust.Scan models. Learning curves are the predominantly used approach in empirical modelling as they show two main benefits: they allow inclusion of different technologies for which specific learning rates are estimated. Furthermore, evidence from ex-post observations and ex-ante estimation match closely.<\\\/p><p>AB  - The paper deals with the concept of learning curves against a broader background of modelling technological change. Based on a literature survey the paper addresses four approaches (endogenous growth theory, learning curves, innovation theory and diffusion theory) and attempts to highlight the links between them. The second part of the paper tackles the empirical implementation of technological change in TranSust.Scan models. Learning curves are the predominantly used approach in empirical modelling as they show two main benefits: they allow inclusion of different technologies for which specific learning rates are estimated. Furthermore, evidence from ex-post observations and ex-ante estimation match closely.<\\\/p><p>M3  - Study<\\\/p><p>BT  - Technological Change and Learning Curves in the Context of the TranSust.Scan Modelling Network. TranSust.Scan Working Paper<\\\/p><p>ER  - <\\\/p><\\\/div>\"}","scientific_assistance":"[]","scientific_review":"[]","version":"","release_date":null,"expiration_date":null,"surveyor":"","research_assistance":"","edv":"","additional_info_de":"","additional_info_en":""},"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v28.0 (Yoast SEO v28.0) - https:\/\/yoast.com\/product\/yoast-seo-premium-wordpress\/ -->\n<title>Technological Change and Learning Curves in the Context of the TranSust.Scan Modelling Network. 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