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The impact of foreign trade and ownership on enterprise exits: new insights through micro data linking

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Abstract

This paper investigates the relationship between enterprise exits, foreign trade and ownership. It uses a unique dataset which is the result of linking enterprise-level data across various domains in the official business statistics of Germany. The study contributes to the literature by showing how micro data linking can be used as a tool by official statistics to explore new areas of statistical analysis without collecting additional data or increasing the administrative burden of enterprises. Further, the richness of the dataset enables us to overcome some of the limitations in existing empirical studies on the topic and to gain additional insights. The results of the empirical analysis show that enterprises engaged in international trade as exporters or importers have lower exit rates than those selling or buying goods solely on the domestic market. Moreover, we find that foreign controlled enterprises have the highest risks to exit the market, while domestic group heads have the lowest. Differences in the exit probabilities between independent and domestically controlled enterprises cannot be observed.

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Notes

  1. Eurostat commissioned Timothy J. Sturgeon (Industrial Performance Center, Massachusetts Institute of Technology) to develop a measurement framework and to identify data gaps and issues arising from economic globalization and to recommend solutions.

  2. The main objectives of the project were to develop methodologies for linking datasets and to produce standardized outputs across the participating countries.

  3. See Jung et al. (2012) for an assessment of the economic importance of foreign owned firms in Germany.

  4. The statistical unit in this study is the enterprise. The terms firms, company or business are used as synonyms.

  5. The official firm data for German (AFiD) is such an example. In the AFiD-project the Research Data Centres have merged official microdata of several surveys and made them available as longitudinal data sets. Further information about AFiD can be obtained on the website of the Research Data Centres of the Federal Statistical Office and the statistical offices of the Länder: http://www.forschungsdatenzentrum.de/en/index.asp. For a comprehensive review on the use of official firm data in Germany for scientific purposes see Wagner (2015).

  6. See Konold (2007) for a detailed description of the surveys.

  7. Background information about the linking project can be found in Eurostat (2015a, 2015b).

  8. In this study, only a subset of the entire MDL database is used for the analysis.

  9. There are a number of other surveys in SBS (e. g. the information and communication usage survey).

  10. EU-Regulation 295/2008, Annex XI. For the methodological background see Eurostat-OECD Manual of Business Demography Statistics, 2007.

  11. For details about the ITGS in Germany see the quality report on foreign trade data published on the website of the Federal Statistical Office of Germany (www.destatis.de).

  12. In 2008, threshold for declarations was 300,000 € in both trade directions (exports, imports). From 1 January 2009 on, the threshold for declarations was increased to 400,000. On 1 January 2012, the limit was set to 500,000 €.

  13. Vogel and Dittrich (2008) describe how exports and imports are covered in the turnover tax statistics.

  14. Details about the linkage between foreign trade data and the BR can be found in Allafi (2011).

  15. The linkage between the datasets is deterministic since the links are solely based on identifiers which are available in the BR and in the other data sources. The use of probabilistic record linkage methods was not necessary.

  16. A more direct measure of the qualification in the workforce would for instance be the share of employees with a university degree. Unfortunately, the current data does not incorporate such information.

  17. In principle, other methods of survival analysis (e. g. cox regression) could be applied to analyze the impact of foreign trade and ownership on market exits. However, since the study covers only a rather short time span (2008–2012) and the number of ties (events at the same recorded time) is large, the use of discrete-time methods such as probit and logit regressions is recommended (see Allison 2010a, 2010b).

  18. Since the dataset covers five years (2008–2012), the cohort of 2008 can be traced over the next four years, while the exit analysis for the cohort of 2011 is limited to the next year.

  19. Since the BD covers a wider range of economic sectors than SBS. Even if an enterprise relocates outside scope of SBS, we are able to track this enterprise over time.

  20. AME have been calculated using the margins command in Stata. Further details about the calculation of AME in Stata and a discussion on the advantages of AME over the use of marginal effects at the mean (MEM) can be found in Williams (2012).

  21. Another way to cope with the rare events structure of the data would be using the procedures suggested in King and Zeng (2001a, 2001b). However, Monte Carlo simulations by Leitgöb (2013) show that the penalized maximum likelihood estimation proposed by Firth is more appropriate. The estimation of a Firth model is conducted using the proc logistic procedure in SAS.

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  1. Federal Statistical Office of Germany, Gustav-Stresemann-Ring 11, 65189, Wiesbaden, Germany

    René Söllner & Sandra Jung

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  1. René Söllner
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  2. Sandra Jung
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Correspondence to René Söllner or Sandra Jung.

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Söllner, R., Jung, S. The impact of foreign trade and ownership on enterprise exits: new insights through micro data linking. AStA Wirtsch Sozialstat Arch 11, 8–32 (2017). https://doi.org/10.1007/s11943-017-0200-1

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