1st ICAI 2020

International Conference on Automotive Industry 2020

Mladá Boleslav, Czech Republic

added, whereas the production phase has lower value added. This is visualized in the so-called smile curve. Value is created by the application of labor, technologies, and organizational expertise to each stage of the production process, and it is a surplus over the costs involved in performing the transformations and transactions at each of these stages (Dicken, 2015). Where the value added comes from, who controls it, benefits from it, and the comparisons between individual countries are of significant interest (de Backer and Miroudot, 2013; Timmer et al., 2013). Further, GVCs are based on a combination of location-specific comparative advantages with firm-specific competitive advantages (Pavlínek et al., 2009). GVCs are driven by technological progress and trade policy reforms. The liberalization and integration of East Asia, as well as the transition economies to the global economy, have contributed to the rise of GVCs (Hilberry, 2011). However, after the financial crisis, we witness a slowing pace of trade and globalization, often referred to as slowbalization. This can be related to trade tensions as well as new technologies and accompanying changes in firm strategies. Now, coronavirus is likely to change the GVCs dramatically. Apart from researchers, global value chains have gained attention also by policymakers in the last decade. This is related to the introduction of datasets, such as OECD- WTO TiVA or WIOD, which provided new data. Based on these datasets, data on the integration into GVCs, as well as other measures, are now available for most developed and emerging economies. The automotive sector belongs amongst the most fragmented industries where the unbundling of production has already been taking place for decades. Further, this industry is under higher political pressure, and the role of the state is significant. The paper aims to sum up the latest research based on value added data in the automotive sector. This will include the description of available datasets and indicators as well as the most influential papers in the automotive industry based on these datasets and their key findings. 2. Data and methodology Traditional trade data do not reflect trade in intermediate products, which are crossing borders several times. This leads to multiple counting. The gross exports were 32% higher than value added exports in 2011, whereas in 1995 it was only 22%higher (OECD, 2015). Traditional trade data also exaggerate the trade imbalances, particularly between China and the US or China and the EU (e.g. Koopman et al., 2010). Intermediate products can also be re-exported to their country of origin for further processing and export. A particular product in the GVCs also often includes inputs from several industries, and trade is getting more intra-industry. Measuring trade in value added is thus crucial to identify the “real” sources of competitiveness and assess the benefits and costs associated with GVCs across sectors and/or social groups. Before the introduction of new datasets based on input output models, GVCs were measured either based on qualitative surveys in companies integrated into GVCs,

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