Business Concentration Data for Germany

  • 1 University of Mannheim, Mannheim, Germany
  • 2 Hamburg University of Applied Sciences, Hamburg, Germany
  • 3 Monopolies Commission, Bonn, Germany
  • 4 Leuphana University Lüneburg, Lüneburg, Germany
Helen Heidorn and John P. WecheORCID iD: https://orcid.org/0000-0003-3801-1699

1 Introduction

The German Monopolies Commission regularly reports on the status and development of business concentration across industries in Germany since its inauguration in 1974. Recently, the Monopolies Commission made panel concentration data available in digital form to researchers and the public. The data is available at a disaggregated 4-digit industry level and considers enterprise groups. It records the industry-specific Herfindahl–Hirschman index values (HHIs) and a concentration ratio (CR6). This manual offers a brief description of the data and available alternatives. Furthermore, it focusses on market structure parameters from an industrial organisation and competition policy perspective. In addition, the manual discusses the validity of interindustry concentration data to facilitate a correct application and interpretation for those not familiar with the respective indicators.

Only recently, economists and competition policy experts paid increasing international attention to the concentration of economic activity, mainly because of a controversial debate in the United States. Empirical studies show that industry concentration has significantly increased in the United States, which has been interpreted as a rise in companies’ market power. 1 If such a trend of industry consolidation can be observed in other economies too, and whether or not it indicates decreasing product market competition, is subject to ongoing research and debate. 2 However, such a trend has not been observed in Germany yet. 3

Until the 1980s, the analysis of concentration data was highly popular in empirical economics. In this context, the structure–conduct–performance (SCP) paradigm, which states market structure primarily influences market behaviour and outcome, was widely accepted. However, in the 1980s economists became aware of the extensive conceptual limitations of the SCP paradigm. They strongly challenged the understanding of a simplified mono-causal relation between industry concentration and competitive intensity (e. g. Demsetz 1973; Posner 1979). Consequently, economists have considered the SCP paradigm as outdated, and although market concentration examinations can be valuable for competition analyses, they demand a careful interpretation (e. g. Schmalensee 1989).

Since its inauguration in 1974, the Monopolies Commission has regularly undertaken various investigations to report on the status and development of business concentration as an indicator of the concentration of economic and political power. In this framework, the Monopolies Commission regularly examines the changing relative economic weight of the 100 largest companies in Germany as well as their personnel and capital linkages. 4 Complementary to measuring this aggregate (i. e. cross-sectoral) concentration, the Monopolies Commission evaluates the degree of industry concentration at a disaggregated level as a proxy for market concentration and market power.

The next section introduces the Herfindahl–Hirschman Index (HHI) and concentration ratio (CR) as two widely used coefficients of business concentration and illustrates the benefits and issues regarding business concentration measures as an indicator of competition intensity. Section 3 describes the data set in detail and considers the availability of historical and alternative data. Section 4 stresses the concerns regarding the validity of concentration statistics, and Section 5 concludes.

2 Concentration measures as indicators of competition intensity

Empirical economic research and antitrust authorities face challenges when it comes to measuring the competitive intensity in a market and the market power of individual companies. This holds especially for interindustry studies, in contrast to merger cases, for instance, where it is not feasible to undertake comprehensive investigations. Calculating concentration measures is a popular method for assessing the degree of competition in a market.

2.1 The Herfindahl–Hirschman Index

The Herfindahl–Hirschman Index (HHI) is a common and regularly applied coefficient of business concentration. It can measure the degree of revenue concentration in a market. The HHI is defined as the sum of the square of the revenue shares (s) of all suppliers (i) in a market (j) for a year (t):

HHIjt=ij,tsijt2

The HHI can reach values between 0 and 10,000. In the context of its horizontal merger guidelines, for example, The United States Department of Justice and the Federal Trade Commission (2010) consider values below 1,500 as unconcentrated markets with high competition. Markets with HHI values between 1,500 and 2,500 are typically classified as moderately concentrated. HHI values exceeding 2,500 indicate high market concentration and might raise significant competitive concerns. Certainly, it should be kept in mind that these thresholds only provide guidelines when interpreting the HHI values. Note also, that in the context of the EU merger control, HHI values are generally evaluated in combination with delta values (European Union 2004). Overall, the HHI is popular due to straightforward calculation and low data requirements.

2.2 Concentration ratios

Concentration ratios (CRs) are another traditional tool for competition economists and authorities to measure market concentration. Analogously to the HHI, the CRn is defined as the sum of the percentage revenue shares (s) of the largest supplier(s) (n) in a market (j) for a year (t):

CRn=ni,j,tsnjt

The CR1, for instance, measures the market share of the largest company, whereas other commonly applied CRs, like the CR4 or CR6, measure the accumulated market share of multiple companies.

The advantage of the CR is the simplicity of both its calculation and interpretation. However, also the CR needs to be handled with care in competition analyses due to various reasons, which are discussed in the following.

2.3 What market concentration can and cannot tell us

There are strong reasons why the concentration of suppliers in a market is a relevant competition parameter and why it is important to pay special attention to highly concentrated markets.

For example, if the number of suppliers in a market decreases, coordinated behaviour becomes easier. The reasons are that the number of potential coordination partners is smaller and the transparency in the market is generally higher. The risk of coordination and cartel creation in concentrated markets is hence higher than in less concentrated markets.

Also, the barriers to entry in highly concentrated markets are typically large for potential new competitors. Barriers to entry can be high fixed costs of production or advertising, as well as patents. Hence, the scope of action for incumbents can be high, which could be used abusively. Incentives for innovation could also be low in such markets, as it may be more difficult for innovative young businesses to enter the market and introduce new ideas and techniques. 5

Another potential risk that is associated with highly concentrated markets is that incumbents may play a dominant role in economic policymaking (e. g. Stigler 1971; Hill et al. 2013; Esty/Caves 1983). This may be problematic, for instance, if companies misuse their political power by maintaining or increasing barriers to entry (e. g. Zingales 2017; Matsumura/Yamagishi 2016).

Finally, a general macroeconomic issue is that highly concentrated markets could also constitute an aggregate risk, especially if they play an important role for the economy overall. The reason is that economic aggregates such as GDP growth and unemployment rates may become dependent on only a few companies. 6

Hence, the investigation of market concentration is crucially relevant not only for competition policy questions. Nonetheless, drawing conclusions about competition intensity from concentration measures can be misleading to a large extent. Some decades ago, in the framework of the structure–conduct–performance (SCP) paradigm, it was popular to assume that the competition intensity in a market is inversely correlated and determined by its concentration (e. g. Bain 1951). The SCP paradigm is today regarded as outdated, as it does not properly account for the interdependency between market structure and firm performance. In a more complex understanding, the market structure may be reversely determined by the firms’ performance. For example, markets may be concentrated due to the realization of economies of scale and scope (e. g. Demsetz 1973; Schmalensee 1989). Therefore, competitors in concentrated markets may not enjoy extensive market power, but instead face strong competition. It follows that business concentration might not necessarily be welfare reducing but instead may deliver advantages to the economy through economies of scale, economies of scope, or low transaction costs.

In conclusion, market concentration measures do not permit any straightforward conclusions regarding the competition intensity in markets. At most, they point to a certain competitive risk potential. An attempt to measure the market power of firms in inter-industry studies more directly is to estimate economic profits from production data. The underlying theoretical idea is that market power manifests itself in prices that exceed marginal costs and is identical with the Lerner index (e. g. Elzinga/Mills 2011). Although market outcome and economic profit analyses are a more direct and preferred method for measuring actual competition intensity, their application in inter industry studies is not without problems, and the data requirements are higher as compared to market concentration analyses. Therefore, often no alternative competition indicators are available or feasible, and concentration measures are therefore still frequently used as indicators for assessing competition in scientific studies and antitrust practice.

3 Concentration data for German industries

The Monopolies Commission recently made concentration data for German industries available in electronic form. The Commission used the data in its 22nd Main Report to analyse the average concentration trend in Germany (Monopolkommission 2018a and 2018b). The data was supplied by the Federal Statistical Office on special behalf of the Monopolies Commission according to § 47 of the German Act against Restraints of Competition, and it covers the period 2007 to 2015. The concentration calculations are representative because they are based on the German business register, which is a full census. The panel can be applied in the context of various research questions and may also serve illustrative and descriptive purposes. 7 It is available from the Monopolies Commission upon request. 8 The following section describes the variables and other characteristics.

3.1 Data description

To date, the panel covers industry concentration data for the period of almost one decade (2007–2015) at a biannual interval. In outline, the data set contains the following core variables for each 4-digit industry: the number of firms, the number of economic units, the sum of the overall revenue, and the two concentration coefficients HHI and CR6 (see Table 1). The data thus includes the HHI and CR6 figures for a total of between 482 and 558 4-digit industries. The industry classification is according to the 2008 Classification of Economic Activities (WZ 2008). 9

Table 1:

Variables.

VariableDescription
wz44-digit industry level. The industry classification is according to the 2008 Classification of Economic Activities (WZ 2008).
Anzahl_WE_WZ4Number of economic units per 4-digit industry level.
Anzahl_RE_WZ4Number of firms per 4-digit industry level.
Summe_Umsatz_in_WZ4Sum of the overall revenue at each 4-digit industry level.
HHIThe Herfindahl-Hirschman Index as coefficient of business concentration. It measures the degree of revenue concentration in a market. The HHI is defined as the sum of the square of the revenue shares of all suppliers in a market for a year. HHI values lie between 0 and 10,000. Low values indicate low market concentration and high values imply high market concentration.
CR6Concentration ratio as a traditional tool to measure market concentration. The CR6 is defined as the sum of the percentage revenue shares of the six largest suppliers in a market for a year.

The companies’ revenue shares that are used to determine each industry’s concentration are based on the individual yearly turnover. A great advantage of the data is that firms in the same industries are aggregated to economic units if they are part of the same enterprise group. Specifically, single companies are assigned to a joint group head based on major common interests and are thus regarded as corporate groups. 10 Hence, the data considers the business concentration in enterprise groups, therefore sector concentration is not underestimated. Because the Federal Statistical Office itself does not record such ownership links, this information is provided by private data providers (e. g. Sturm et al. 2009). 11

3.2 Historical data to 1973 and alternative sources

In general, concentration data and analyses are available in reports by the Monopolies Commission since 1973. However, the exploitation may be cumbersome, as data for the years 1973–2001 is available only in the printed versions of the Monopolies Commission’s Main Reports. Concentration data for the years 2003–2015 is published in the Main Reports in PDF format and available on the website of the Monopolies Commission. 12 However, there have been fundamental methodological changes over time (as discussed in the respective reports), which pose extensive obstacles for the comparability of the data. Older data does not consider enterprise groups and the industry classification changes over time also the Monopolies Commission has put a focus on certain industries in past years.

There are additional sources that provide concentration data for Germany. The Federal Statistical Office offers business concentration data on specific sectors, which are the manufacturing sector, the mining industry, and the construction business. 13 Also, in the context of the research project EU KLEMS (2008), industry concentration data was published for selected European countries for the years 1997 to 2006. The data covers HHI figures for up to 43 sectors, mostly 2-digit industries. 14 Moreover, in 2014 the research network CompNet published data on the concentration rate of the 10 largest companies of a sector in addition to HHI values (Lopez-Garcia/Di Mauro 2015). The data covers the period 1995 to 2012 and includes 56 industries in 17 EU countries.

4 Validity concerns

Despite the concerns regarding the informative value of concentration measures as an indicator for competition intensity (as explained in Section 2), there are also severe concerns with respect to the validity of HHI figures by industry classifications as a statistical indicator of market concentration – potential liabilities that all interindustry concentration data have in common (e. g. Hunold et al. 2011).

First, a clear market definition that is based on the respective economic conditions is essential for a concentration measure to indicate market concentration. Thus, a market must be defined by product specificity as well as geographically. In single-market analyses in merger control, for example, a commonly used method is the SSNIP test, which tests for the substitutability of products in order to accurately segregate product markets. However, such methods – albeit controversial on their own – cannot be applied in inter-industry studies. Instead, industry classifications for statistical purposes are used to approximate market definitions, but they do not fulfil the criteria of economically defined markets.

Second, in structural business statistics it is usually not possible to break down corporate turnover into specific areas of activity. Hence, statistical offices resort to assigning total company turnovers (based on the sales principle) to the sector where most of their revenue is generated. It follows that their economic weight is overestimated in their assigned main industry and underestimated in others. Furthermore, the assignment might differ over the years, which could lead to significant fluctuations of the estimated sector concentration.

Finally, the majority of surveys capture market volumes based exclusively on national sales and do not take account of foreign trade activities, meaning exports and imports, at all.

Overall, there are various technical limitations to the data set that might lead to serious distortions regarding the empirical measurement of market concentration across economic sectors.

5 Conclusion

The Monopolies Commission regularly publishes investigations of industry concentration in Germany and has recently made concentration data available to researchers and the public for the years 2007–2015. The data captures the degree of business concentration for a total of up to 558 industries according to the 4-digit industry level. The data covers the Herfindahl–Hirschman index and the concentration rate of the six largest economic units (CR6) for each industry and considers enterprise groups by combining single firms to corporate groups. This document provides a brief manual for this data and discusses the advantages and disadvantages of using concentration data for competition analyses. Further, it highlights the serious limitations concerning concentration measures as an indicator of the competition intensity in markets.

References

  • Aghion, P., N. Bloom, R. Blundell, R. Griffith, P. Howitt (2005), Competition and Innovation: An Inverted-U Relationship. The Quarterly Journal of Economics 120 (2): 701–728.

  • Autor, D., D. Dorn, L.F. Katz, C. Patterson, J. Van Reenen (2017), The Fall of the Labor Share and the Rise of Superstar Firms, NBER Working Paper 23396, May 2017.

  • Bain, J.S. (1951), Relation of Profit Rate to Industry Concentration: American Manufacturing, 1936–1940. The Quarterly Journal of Economics 65 (3): 293–324.

    • Crossref
    • Export Citation
  • Bajgar, M., G. Berlingieri, S. Calligaris, C. Criscuolo, J. Timmis (2019), Industry Concentration in Europe and North America, OECD Productivity Working Papers, 2019-18, OECD Publishing, Paris.

  • Buchwald, A., R. Hotten, J. Rothbauer, J.P. Weche (2020), The Top 100 Companies Panel Database, Five Decades of Aggregate Concentration Surveys in Germany. Jahrbücher für Nationalökonomie und Statistik/Journal of Economics and Statistics, forthcoming.

  • Demsetz, H. (1973), Industry Structure, Market Rivalry and Public Policy. The Journal of Law and Economics 16 (1): 1–9.

    • Crossref
    • Export Citation
  • Deutsche Bundesbank (2017), Monatsbericht Dezember 2017, 69. Jahrgang Nr. 12, 53–68, https://www.bundesbank.de/resource/blob/665596/1c04844fbb808405b11309bae5d4d7b7/mL/2017-12-monatsbericht-data.pdf, July 2019.

  • Elzinga, K.G., D.E. Mills (2011), The Lerner Index of Monopoly Power: Origins and Uses. American Economic Review 101 (3): 558–564.

    • Crossref
    • Export Citation
  • Esty, D.C., R.E. Caves (1983), Market Structure and Political Influence: New Data on Political Expenditures, Activity, and Success. Economic Enquiry 21 (1): 24–38.

    • Crossref
    • Export Citation
  • EU KLEMS (2008), Linked Data 2008 Release [Database], Company Accounts, http://www.euklems.net/linked.shtml, July 2019.

  • European Union (2004), Guidelines on the Assessment of Horizontal Mergers under the Council Regulation on the Control of Concentrations between Undertakings, Official Journal of the European Union, 2004/C 31/03, https://eur-lex.europa.eu/legal-content/EN/TXT/PDF/?uri=CELEX:52004XC0205(02)&from=EN, July 2019.

  • Furman, J., P. Orszag (2015), A Firm-Level Perspective on the Role of Rents in the Rise in Inequality, Presentation at “A Just Society” Centennial Event in Honor of Joseph Stiglitz at Columbia University, October 16.

  • Gabaix, X. (2011), The Granular Origins of Aggregate Fluctuations. Econometrica 79 (3): 733–772.

    • Crossref
    • Export Citation
  • Grullon, G., Y. Larkin, R. Michaely (2019), Are US Industries Becoming More Concentrated. Review of Finance 23 (4): 697–743.

    • Crossref
    • Export Citation
  • Hashmi, A.R. (2013), Competition and Innovation: the Inverted-U Relationship Revisited. Review of Economics and Statistics 95 (5): 1653–1668.

    • Crossref
    • Export Citation
  • Hill, M.D., G.W. Kelly, G.B. Lockhart, R.A. Van Ness (2013), Determinants and Effects of Corporate Lobbying. Financial Management 42 (4): 931–957.

    • Crossref
    • Export Citation
  • Hunold, M., U. Laitenberger, G. Licht, V. Nikogosian, A. Stenzel, H. Ullrich, C. Wolf (2011), Modernisierung der Konzentrationsberichterstattung, Bundesministerium für Wirtschaft und Technologie (BMWi), Mannheim.

  • Lopez-Garcia, P., F. Di Mauro (2015), Assessing European Competitiveness: The New CompNet Micro-based Database, ECB Working Paper Series, No. 1764.

  • Matsumura, T., A. Yamagishi (2016), Lobbying for Regulation Reform by Industry Leaders. Journal of Regulatory Economics 52 (1): 63–76.

  • Monopolkommission (2018a), XXII. Hauptgutachten. Wettbewerb 2018. Baden-Baden, Nomos.

  • Monopolkommission (2018b), Trends in Indicators of Market Power in Germany and Europe, Excerpt from Chapter II of the XXII. Biennial Report of the Monopolies Commission (“competition 2018”) in Accordance with Section 44 Paragraph 1 Sentence 1 of the German Act against Restraints of Competition, https://www.monopolkommission.de/images/HG22/Main_Report_XXII_Market_Power.pdf.

  • O’Mahony, M., C. Castaldi, B. Los, E. Bartelsman, Y. Maimaiti, F. Peng (2008), EUKLEMS – Linked Data: Sources and Methods, University of Birmingham, October 2008.

  • OECD (2018), Market Concentration, Issues Paper by the Secretariat, DAF/COMP/WD(2018)46, 20. April 2018.

  • Posner, R.A. (1979), The Chicago School of Antitrust Law. The University of Pennsylvania Law Review 127: 925–948.

    • Crossref
    • Export Citation
  • Schmalensee, R. (1989), Inter-industry Studies of Structure and Performance. Handbook of Industrial Organization 2: 951–1009, North-Holland, Elsevier.

    • Crossref
    • Export Citation
  • Stigler, G.J. (1971), The Theory of Economic Regulation. The Bell Journal of Economics and Management Science 2 (1): 3–21.

    • Crossref
    • Export Citation
  • Sturm, R., T. Tümmler, R. Opfermann (2009), Unternehmensverflechtungen Im Statistischen Unternehmensregister. Wirtschaft und Statistik 8: 764–773.

  • The U.S. Department of Justice and the Federal trade Commission (2010), Horizontal Merger Guidelines, Antitrust Division, https://www.justice.gov/sites/default/files/atr/legacy/2010/08/19/hmg-2010.pdf, July 2019.

  • Wagner, J. (2012), The German Manufacturing Sector Is a Granular Economy. Applied Economics Letters 19 (17): 1663–1665.

    • Crossref
    • Export Citation
  • Wagner, J., J.P. Weche (2020), On the Granularity of the German Economy – First Evidence from the Top 100 Companies Panel Database. Applied Economics Letters, forthcoming (online first).

  • Wambach, A., J.P. Weche (2020), Sektorübergreifende Konzentrations- und Margenzunahme: Bestandsaufnahme, Ursachen und Folgen. Perspektiven der Wirtschaftspolitik, forthcoming.

  • Weche Gelübcke, J.P. (2011), Ownership Patterns and Enterprise Groups in German Structural Business Statistics. Schmollers Jahrbuch 131: 635–647.

    • Crossref
    • Export Citation
  • Weche, J.P., J. Wagner (2020), Markups and Concentration in the Context of Digitization: Evidence from German Manufacturing Industries. mimeo.

  • Zingales, L. (2017), Towards a Political Theory of the Firm. The Journal of Economic Perspectives 31 (3): 113–130.

    • Crossref
    • Export Citation

Footnotes

2

See Bajgar et al. (2019) for evidence and OECD (2018) for a general overview. Wambach and Weche (2020) provide a recent literature survey that focuses on Germany (in German language).

3

For comprehensive analyses of industry concentration in Germany, see Monopolkommission (2018a) or the English translation Monopolkommission (2018b) and Deutsche Bundesbank (2017).

4

A panel dataset of the Top 100 German firms (1972–2016) has also recently become available. See Buchwald et al. (2020) for further information.

5

However, the relationship between competition and innovation appears to be complex and not straightforward. See e. g. Aghion et al. (2005) and Hashmi (2013) for a general discussion and empirical assessment.

6

See Gabaix (2011) for an empirical assessment of the “granularity hypothesis” and Wagner (2012) as well as Wagner and Weche (2020) for empirical evidence for Germany.

7

For example, Weche and Wagner (2020) use this data to investigate the concentration development in German manufacturing industries and contrast concentration figures with economic price–cost margins.

10

For a detailed description of the detection of enterprise groups in German business statistics, see Weche Gelübcke (2011).

11

Note that the respective information for corporate groups is provided by one private data supplier for the years 2007, 2009, 2011, and 2013 and by another provider for 2015. Thus, the comparability of the respective reporting years may be restricted.

14

See O’Mahony et al. (2008) for further information.

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  • Aghion, P., N. Bloom, R. Blundell, R. Griffith, P. Howitt (2005), Competition and Innovation: An Inverted-U Relationship. The Quarterly Journal of Economics 120 (2): 701–728.

  • Autor, D., D. Dorn, L.F. Katz, C. Patterson, J. Van Reenen (2017), The Fall of the Labor Share and the Rise of Superstar Firms, NBER Working Paper 23396, May 2017.

  • Bain, J.S. (1951), Relation of Profit Rate to Industry Concentration: American Manufacturing, 1936–1940. The Quarterly Journal of Economics 65 (3): 293–324.

    • Crossref
    • Export Citation
  • Bajgar, M., G. Berlingieri, S. Calligaris, C. Criscuolo, J. Timmis (2019), Industry Concentration in Europe and North America, OECD Productivity Working Papers, 2019-18, OECD Publishing, Paris.

  • Buchwald, A., R. Hotten, J. Rothbauer, J.P. Weche (2020), The Top 100 Companies Panel Database, Five Decades of Aggregate Concentration Surveys in Germany. Jahrbücher für Nationalökonomie und Statistik/Journal of Economics and Statistics, forthcoming.

  • Demsetz, H. (1973), Industry Structure, Market Rivalry and Public Policy. The Journal of Law and Economics 16 (1): 1–9.

    • Crossref
    • Export Citation
  • Deutsche Bundesbank (2017), Monatsbericht Dezember 2017, 69. Jahrgang Nr. 12, 53–68, https://www.bundesbank.de/resource/blob/665596/1c04844fbb808405b11309bae5d4d7b7/mL/2017-12-monatsbericht-data.pdf, July 2019.

  • Elzinga, K.G., D.E. Mills (2011), The Lerner Index of Monopoly Power: Origins and Uses. American Economic Review 101 (3): 558–564.

    • Crossref
    • Export Citation
  • Esty, D.C., R.E. Caves (1983), Market Structure and Political Influence: New Data on Political Expenditures, Activity, and Success. Economic Enquiry 21 (1): 24–38.

    • Crossref
    • Export Citation
  • EU KLEMS (2008), Linked Data 2008 Release [Database], Company Accounts, http://www.euklems.net/linked.shtml, July 2019.

  • European Union (2004), Guidelines on the Assessment of Horizontal Mergers under the Council Regulation on the Control of Concentrations between Undertakings, Official Journal of the European Union, 2004/C 31/03, https://eur-lex.europa.eu/legal-content/EN/TXT/PDF/?uri=CELEX:52004XC0205(02)&from=EN, July 2019.

  • Furman, J., P. Orszag (2015), A Firm-Level Perspective on the Role of Rents in the Rise in Inequality, Presentation at “A Just Society” Centennial Event in Honor of Joseph Stiglitz at Columbia University, October 16.

  • Gabaix, X. (2011), The Granular Origins of Aggregate Fluctuations. Econometrica 79 (3): 733–772.

    • Crossref
    • Export Citation
  • Grullon, G., Y. Larkin, R. Michaely (2019), Are US Industries Becoming More Concentrated. Review of Finance 23 (4): 697–743.

    • Crossref
    • Export Citation
  • Hashmi, A.R. (2013), Competition and Innovation: the Inverted-U Relationship Revisited. Review of Economics and Statistics 95 (5): 1653–1668.

    • Crossref
    • Export Citation
  • Hill, M.D., G.W. Kelly, G.B. Lockhart, R.A. Van Ness (2013), Determinants and Effects of Corporate Lobbying. Financial Management 42 (4): 931–957.

    • Crossref
    • Export Citation
  • Hunold, M., U. Laitenberger, G. Licht, V. Nikogosian, A. Stenzel, H. Ullrich, C. Wolf (2011), Modernisierung der Konzentrationsberichterstattung, Bundesministerium für Wirtschaft und Technologie (BMWi), Mannheim.

  • Lopez-Garcia, P., F. Di Mauro (2015), Assessing European Competitiveness: The New CompNet Micro-based Database, ECB Working Paper Series, No. 1764.

  • Matsumura, T., A. Yamagishi (2016), Lobbying for Regulation Reform by Industry Leaders. Journal of Regulatory Economics 52 (1): 63–76.

  • Monopolkommission (2018a), XXII. Hauptgutachten. Wettbewerb 2018. Baden-Baden, Nomos.

  • Monopolkommission (2018b), Trends in Indicators of Market Power in Germany and Europe, Excerpt from Chapter II of the XXII. Biennial Report of the Monopolies Commission (“competition 2018”) in Accordance with Section 44 Paragraph 1 Sentence 1 of the German Act against Restraints of Competition, https://www.monopolkommission.de/images/HG22/Main_Report_XXII_Market_Power.pdf.

  • O’Mahony, M., C. Castaldi, B. Los, E. Bartelsman, Y. Maimaiti, F. Peng (2008), EUKLEMS – Linked Data: Sources and Methods, University of Birmingham, October 2008.

  • OECD (2018), Market Concentration, Issues Paper by the Secretariat, DAF/COMP/WD(2018)46, 20. April 2018.

  • Posner, R.A. (1979), The Chicago School of Antitrust Law. The University of Pennsylvania Law Review 127: 925–948.

    • Crossref
    • Export Citation
  • Schmalensee, R. (1989), Inter-industry Studies of Structure and Performance. Handbook of Industrial Organization 2: 951–1009, North-Holland, Elsevier.

    • Crossref
    • Export Citation
  • Stigler, G.J. (1971), The Theory of Economic Regulation. The Bell Journal of Economics and Management Science 2 (1): 3–21.

    • Crossref
    • Export Citation
  • Sturm, R., T. Tümmler, R. Opfermann (2009), Unternehmensverflechtungen Im Statistischen Unternehmensregister. Wirtschaft und Statistik 8: 764–773.

  • The U.S. Department of Justice and the Federal trade Commission (2010), Horizontal Merger Guidelines, Antitrust Division, https://www.justice.gov/sites/default/files/atr/legacy/2010/08/19/hmg-2010.pdf, July 2019.

  • Wagner, J. (2012), The German Manufacturing Sector Is a Granular Economy. Applied Economics Letters 19 (17): 1663–1665.

    • Crossref
    • Export Citation
  • Wagner, J., J.P. Weche (2020), On the Granularity of the German Economy – First Evidence from the Top 100 Companies Panel Database. Applied Economics Letters, forthcoming (online first).

  • Wambach, A., J.P. Weche (2020), Sektorübergreifende Konzentrations- und Margenzunahme: Bestandsaufnahme, Ursachen und Folgen. Perspektiven der Wirtschaftspolitik, forthcoming.

  • Weche Gelübcke, J.P. (2011), Ownership Patterns and Enterprise Groups in German Structural Business Statistics. Schmollers Jahrbuch 131: 635–647.

    • Crossref
    • Export Citation
  • Weche, J.P., J. Wagner (2020), Markups and Concentration in the Context of Digitization: Evidence from German Manufacturing Industries. mimeo.

  • Zingales, L. (2017), Towards a Political Theory of the Firm. The Journal of Economic Perspectives 31 (3): 113–130.

    • Crossref
    • Export Citation