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  1. Does updating education curricula accelerate technology adoption in the workplace?
    evidence from dual vocational education and training curricula in Switzerland
    Autor*in: Schultheiss, Tobias; Backes-Gellner, Uschi
    Erschienen: October 2022
    Verlag:  IZA - Institute of Labor Economics, Bonn, Germany

    In an environment of accelerating technological change and increasing digitalization, firms need to adopt new technologies faster than ever before to stay competitive. This paper examines whether updates of education curricula help to bring new... mehr

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    ZBW - Leibniz-Informationszentrum Wirtschaft, Standort Kiel
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    DS 4
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    In an environment of accelerating technological change and increasing digitalization, firms need to adopt new technologies faster than ever before to stay competitive. This paper examines whether updates of education curricula help to bring new technologies faster into firms' workplaces. We study technology changes and curriculum updates from an early wave of digitalization (i.e., computernumerically controlled machinery, computer-aided design, and desktop publishing software). We take a text-as-data approach and tap into two novel data sources to measure change in educational content and the use of technology at the workplace: first, vocational education curricula and, second, firms' job advertisements. To examine the causal effects of adding new technology skills to curricula on the diffusion of these technologies in firms' workplaces (measured by job advertisements), we use an event study design. Our results show that curriculum updates substantially shorten the time it takes for new technologies to arrive in firms' workplaces, especially for mainstream firms.

     
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    Quelle: Verbundkataloge
    Sprache: Englisch
    Medientyp: Buch (Monographie)
    Format: Online
    Weitere Identifier:
    hdl: 10419/267426
    Schriftenreihe: Discussion paper series / IZA ; no. 15689
    Schlagworte: technological change; digitalization; curricula updates; technology diffusion; text-as-data
    Umfang: 1 Online-Ressource (circa 75 Seiten), Illustrationen
  2. A macroscope of English print culture, 1530-1700, applied to the coevolution of ideas on religion, science, and institutions
    Autor*in: Grajzl, Peter; Murrell, Peter
    Erschienen: March 2023
    Verlag:  CESifo, Munich, Germany

    We combine unsupervised machine-learning and econometric methods to examine cultural change in 16th- and 17th-century England. A machine-learning digest synthesizes the content of 57,863 texts comprising 83 million words into 110 topics. The topics... mehr

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    We combine unsupervised machine-learning and econometric methods to examine cultural change in 16th- and 17th-century England. A machine-learning digest synthesizes the content of 57,863 texts comprising 83 million words into 110 topics. The topics include the expected, such as Natural Philosophy, and the unexpected, such as Baconian Theology. Using the data generated via machine-learning we then study facets of England's cultural history. Timelines suggest that religious and political discourse gradually became more scholarly over time and economic topics more prominent. The epistemology associated with Bacon was present in theological debates already in the 16th century. Estimating a VAR, we explore the coevolution of ideas on religion, science, and institutions. Innovations in religious ideas induced strong responses in the other two domains. Revolutions did not spur debates on institutions nor did the founding of the Royal Society markedly elevate attention to science.

     
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    Weitere Identifier:
    hdl: 10419/271969
    Schriftenreihe: CESifo working papers ; 10325 (2023)
    Schlagworte: cultural history; England; machine-learning; text-as-data; coevolution; VAR
    Umfang: 1 Online-Ressource (circa 116 Seiten), Illustrationen
  3. Analyzing climate change policy narratives with the character-role narrative framework
    Autor*in: Gehring, Kai; Grigoletto, Matteo
    Erschienen: May 2023
    Verlag:  CESifo, Munich, Germany

    Understanding behavioral aspects of collective decision-making is an important challenge for eco-nomics, and narratives are a crucial group-based mechanism that influences human decision-making. This paper introduces the Character-Role Narrative... mehr

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    Understanding behavioral aspects of collective decision-making is an important challenge for eco-nomics, and narratives are a crucial group-based mechanism that influences human decision-making. This paper introduces the Character-Role Narrative Framework as a tool to systematically analyze narratives, and applies it to study US climate change policy on Twitter over the 2010-2021 period. We build on the idea of the so-called drama triangle that suggests, within the context of a topic, the essence of a narrative is captured by its characters in one of three essential roles: hero, villain, and victim. We show how this intuitive framework can be easily integrated into an empirical pipeline and scaled up to large text corpora using supervised machine learning. In our application to US climate change policy narratives, we find strong changes in the frequency of simple and complex character-role narratives over time. Using contagiousness, popularity, and sparking conversation as three distinct dimensions of virality, we show that narratives that are simple, feature human characters and emphasize villains tend to be more viral. Focusing on Donald Trump as an example of a populist leader, we demonstrate that populism is linked to a higher share of such simple, human, and villain-focused narratives.

     
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    hdl: 10419/279178
    Schriftenreihe: CESifo working papers ; 10429 (2023)
    Schlagworte: narrative economics; text-as-data; machine learning; large language models; climate change; virality; populism
    Umfang: 1 Online-Ressource (circa 85 Seiten), Illustrationen
  4. Construction and analysis of uncertainty indices based on multilingual text representations
    Autor*in: Naboka-Krell, Viktoriia
    Erschienen: [2023]
    Verlag:  Philipps-University Marburg, School of Business and Economics, Marburg

    The work by Baker et al. (2016), who propose a dictionary based method and estimate the level of economic policy uncertainty (EPU) based on the occurrence of specific terms in ten leading newspapers in the USA, is among the first ones to detect the... mehr

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    The work by Baker et al. (2016), who propose a dictionary based method and estimate the level of economic policy uncertainty (EPU) based on the occurrence of specific terms in ten leading newspapers in the USA, is among the first ones to detect the potential of text data in economic research. Following this line of research, this paper proposes automated approaches to construction of EPU indices for different countries based on newspapers' texts. First, multilingual fastText word embeddings and BERT text embeddings are used in order to define relevant EPU key words and EPU related articles, respectively. Further, multilingual conceptualized topic modeling introduced by Bianchi et al. (2021) is performed and EPU related topics are detected. It is shown that the constructed EPU indices based on fastText embeddings Granger cause the economic activity in all of the considered countries, namely Germany, Russia, and Ukraine. Also, some of the topics uncovered by multilingual conceptualized topic modeling have proved to Granger cause the economic activity in all of the considered countries.

     
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    Sprache: Englisch
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    hdl: 10419/278434
    Schriftenreihe: Joint discussion paper series in economics ; no. 2023, 10
    Schlagworte: text-as-data; fastText emeddings; BERT; economic policy uncertainty; natural language processing
    Umfang: 1 Online-Ressource (circa 24 Seiten), Illustrationen
  5. Regulatory compliance with limited enforceability
    evidence from privacy policies
    Autor*in: Ganglmair, Bernhard; Krämer, Julia; Gambato, Jacopo
    Erschienen: [2024]
    Verlag:  ZEW - Leibniz Centre for European Economic Research, Mannheim, Germany

    The EU General Data Protection Regulation (GDPR) of 2018 introduced stringent transparency rules compelling firms to disclose, in accessible language, details of their data collection, processing, and use. The specifics of the disclosure requirement... mehr

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    The EU General Data Protection Regulation (GDPR) of 2018 introduced stringent transparency rules compelling firms to disclose, in accessible language, details of their data collection, processing, and use. The specifics of the disclosure requirement are objective, and its compliance is easily verifiable; readability, however, is subjective and difficult to enforce. We use a simple inspection model to show how this asymmetric enforceability of regulatory rules and the corresponding firm compliance are linked. We then examine this link empirically using a large sample of privacy policies from German firms. We use text-as-data techniques to construct measures of disclosure and readability and show that firms increased the disclosure volume, but the readability of their privacy policies did not improve. Larger firms in concentrated industries demonstrated a stronger response in readability compliance, potentially due to heightened regulatory scrutiny. Moreover, data protection authorities with larger budgets induce better readability compliance without effects on disclosure.

     
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    hdl: 10419/289447
    Schriftenreihe: Discussion paper / ZEW ; no. 24, 012 (03/2024)
    Schlagworte: data protection; disclosure; GDPR; privacy policies; readability; regulation; text-as-data; topic models
    Umfang: 1 Online-Ressource (circa 68 Seiten), Illustrationen
  6. Understanding multi-layered sanctions
    a firm-level analysis
    Autor*in: Shamsi, Javad
    Erschienen: [2023]
    Verlag:  Centre for Economic Performance, London School of Economics and Political Science, London

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    Schriftenreihe: Discussion paper / Centre for Economic Performance ; no. 1956 (November 2023)
    Schlagworte: economic sanctions; firms; text-as-data; computational linguistics
    Umfang: 1 Online-Ressource (circa 38 Seiten), Illustrationen
  7. Text-as-data analysis of preferential trade agreements
    mapping the PTA landscape
    Autor*in: Alschner, Wolfgang; Seiermann, Julia; Skougarevskiy, Dmitriy
    Erschienen: 2017
    Verlag:  The Graduate Institute Geneva, Center for Trade and Economic Integration, Genève

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    ZBW - Leibniz-Informationszentrum Wirtschaft, Standort Kiel
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    Quelle: Verbundkataloge
    Sprache: Englisch
    Medientyp: Buch (Monographie)
    Format: Online
    Schriftenreihe: CTEI working papers ; CTEI-2017, 13
    Schlagworte: preferential trade agreements; free trade agreements; text-as-data; similarity; computational analysis of law; trade law; WTO
    Umfang: 1 Online-Ressource (circa 39 Seiten), Illustrationen