Abstract
In this paper, I study the consequences of globalization, as measured by the involvement of firms in GVC, on the business environment. In particular, I focus on concentration and productivity, firstly by estimating robust elasticities and then isolating the exogenous component of the variation in the participation in GVC. To this aim, I exploit the distance between industries in terms of upstreamness and downstreamness along the supply chain. The evidences suggest that involvement in international supply chains is positively related to concentration at the sector level and positively associated with aggregate productivity, an effect that is driven by the firms at the top of the productivity distribution. Finally, I relate these findings to the current pandemic, going beyond the lack of official statistics and estimating GVC participation for 2020 at the country-level through real time world-seaborne trade data, providing evidences on the re-absorption of the Covid shock in several European economies.
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Notes
- 1.
Issues have been found in defining—and hence measuring—thoroughly globalization. To this aim, various indicators have been proposed starting from the interaction of various metrics, such as movement of goods (trade), people (migration) and of capital, but none has been recognized as systematically better than the others (Dreher et al., 2008). Here, I will focus on globalization in the form of trade of goods, in particular in the form of GVC.
- 2.
For example, see experts’ opinions expressed in this interview: Have We Reached Peak Globalization?, Bloomberg News, January 24, 2020.
- 3.
A detailed description of all the data employed in this study can be found in the appendix.
- 4.
The industry aggregations of the CompNet and WIOD dataset do not coincide precisely and hence some further aggregation is needed to perform an exact match of the two datasets. A detailed list of the 2-digit sectors available in the two datasets is available in the appendix.
- 5.
Please note that all the models presented here are fixed effect models, therefore each result needs to be interpreted as the increase (decrease) of y within a country-sector-year associated with the increase of x.
- 6.
For example, the sector 10, 11 and 12 (Manufacture of respectively food, beverages and tobacco) are aggregated in only one figure in WIOD, they are separated in the CompNet dataset.
- 7.
Since I focus on the European region, I randomly take a Southern region (Italy), a Northern one (Germany) and an Eastern one (Hungary).
- 8.
The models studying the OP gap are the only ones yet presented that have the dependent variable in levels instead of log. This is simply due to the fact that the covariance measure (covariance between productivity and market shares) can be either negative or positive. When it is negative, it shows that the allocation of production factors is not efficient, namely that less productive firms have larger market shares. By taking the logarithm of this measure, I would exclude all the negative values.
- 9.
Here, the same caveat of the results presented in Table 2 applies. The number of sectors used in this analysis is lower due to issues in the harmonization of different datasets.
- 10.
Hoffmann and Sirimanne (2017) estimate that over 80% of global merchandise trade by volume and more than 70% of its value can be traced to maritime transport.
- 11.
Firm-level information is typically not available since it is confidential. Therefore, cross-country comparability is often hampered because data are stored by national statistical institutes. Often, the definition of variables may change, too.
- 12.
For example this means that I can compare the exporters’ distribution of productivity with the one of domestic firms. This will help me in estimating productivity premia for firms engaged in international trade.
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Acknowledgements
I am extremely grateful to Filippo di Mauro and Carlo Altomonte for useful comments. CompNet provided me with the data, and all the CompNet scientific team helped me to exploit better this rich dataset. All errors are my own.
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Appendix
Appendix
1.1 Data Sources
For this paper I used several data sources, exploiting micro-aggregated variables. In particular, I used the CompNet dataset, the World Input Output Database and the UN ESCAP dataset. This section is intended to briefly present each data source.
The CompNet dataset is the main product of the Competitive Research Network. It provides granular and micro-aggregated data overcoming the harmonization and confidentiality issues through the micro-distributed approach (Lopez-Garcia & di Mauro, 2015). The dataset presents data at the country, size, 2-digit sector and NUTS2 level. Tables 7 and 8 present the sample composition in terms of countries and industries. CompNet provides its dataset without ever accessing the micro-data, that are safely stored by the Data Providers and therefore avoiding confidentiality issues.Footnote 11 Notwithstanding this issue, harmonization of the raw variables is ensured by the CompNet research team, that works alongside the Data Providers to ensure the best data quality (CompNet, 2020b). The data collection process works in the following way: CompNet sends a harmonized data gathering protocol to collect and calculate various variables and indicators to several data providers (one for each of the 19 European countries in the dataset). The data gathering protocol computes the desired micro-aggregate indicators which are then sent back to the Scientific Staff of CompNet that subsequently builds the CompNet database from the micro-aggregate indicators. A particular feature of this dataset that I will exploit is that it collects joint distributions, i.e. conditional distributions of some variable given a specific condition, that can be either discrete or continuous.Footnote 12
On the other hand, the World Input-Output Database (WIOD) is constituted by annual time-series of world input-output tables from 1995 to 2014. World Input-Output Tables and underlying data, cover 43 countries plus the fictional “Rest of the World” region, that comprises the residual countries of the world. Data for 56 sectors are classified according to the International Standard Industrial Classification revision 4 (ISIC Rev. 4). These tables have been constructed in a clear conceptual framework based on the system of national accounts. They are based on officially published input-output tables merged with national accounts data and international trade statistics (Timmer et al., 2015). A WIOT provides a comprehensive summary of all transactions in the global economy between industries and final users across countries. In addition to a national input-output table, imports are broken down according to the country and industry of origin in a WIOT in order to allow a user to retrieve domestic and foreign value added. Tables 9 and 10 provide the sample composition in terms of countries and industries present in the WIOD (Tables 9 and 10).
Finally, bilateral trade costs data is taken from the UNESCAP-World Bank Trade Cost Database. It estimates bilateral trade costs on the basis of the model developed by Novy (2013), which estimates trade costs for each country pair using bilateral trade and gross national output. It collects information for over 200 countries, with observations ranging from 1995 to 2018. Through the methodology employed in retrieving the trade costs data, it gives a micro-founded comprehensive trade cost figure that includes both structural factors, such as geography, and policy measures, such as tariffs (Kummritz, 2016). Differences in economic size and endowments are not the only reason why some countries trade more than others: trade flows depend on many other factors that express the degree of separation between countries, such as the aforementioned geography and policy measure. A more detailed description of the database can be found in Arvis et al. (2013).
1.2 Additional Tables and Figures
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Inferrera, S. (2022). Globalization in Europe: Consequences for the Business Environment and Future Patterns in Light of Covid-19. In: Paganetto, L. (eds) Economic Challenges for Europe After the Pandemic. Springer Proceedings in Business and Economics. Springer, Cham. https://doi.org/10.1007/978-3-031-10302-5_6
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