KSG: Indicator of Economic Concentration

Manuel Meireles

Article ID: 829
Vol 5, Issue 1, 2022

VIEWS - 1783 (Abstract) 675 (PDF)


In the fields of Management and Economics, there are many studies that have made use of the degree of concentration of a market or industry, especially when dealing with subjects such as industrial concentration. However, these indexes do not adequately present the level of significance. This problem is overcome by the proposed KSG indicator based on the Kolmogorov-Smirnov test and whose interpretation of significance is given by Goodman. Hence the name: KSG. The proposed model uses non-parametric techniques to establish the dimension of concentration and defines the level of significance of the value found. This is a quantitative study using parametric statistics (polynomial regression) on data generated through simulation. In each data simulation, for the given value of n companies, the share of Company 1 is made to vary, with the other shares being maintained unchanged. For each simulation, data related to values of "Share of Company 1" were extracted and corresponding indexes: KSG, CR4, CR8 and HHI. The results show that the indicator proposed in this study is fully justified.


Indicator of economic concentration; Competitiveness; Imperfect markets;Market concentration; KSG

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1. Lerner PA. The concept of monopoly and the measurement of monopoly power. Review of Economic Studies 1934; 1(3): 157-175.

2. Iwata G. Measurement of conjectural variation in oligopoly. Econometrica 1974; 42(5): 947-966.

3. Braga H, Mascolo J. Mensuração da concentração industrial no Brasil. Pesquisa e PlanejamentoEconômico 1982; 12(2): 399-454.

4. Tirole J. A teoria da organização industrial. Cambridge, Mas.: MIT Press 1988.

5. Scherer FM, Ross D. Industrial market structure and economic performance. (3a ed.). Boston: Houghton Mifflin 1990.

6. Tonge SD, Wooton CW. (1991). Auditor concentration and competition among the large

7. public accounting firms: post-merger status and future implications. Journal of Accounting and Public Policy 1991; 10: 157-172.

8. Baker JB, Bresnahan TF. Empirical methods of identifying and measuring market power. Antitrust Law Journal 1992; 61(1): 3-16.

9. Cuevas F. La Reglamentacion de un Monopólio Natural: El Caso de La IndustriaElectricaen América Latina; UnEnfoquePolítico-Econômico. Tese de doutorado, Universidade de Montpellier I, Montpellier, França 1993.

10. Tiffin AL, Dawson PJ. (1997). Measuring oligopolistic distortion in the UK frozen potato product sector: a calibration modeling approach. Journal of Agricultural Economics 1997; 48(3): 300-312.

11. Borenstein S, Bushnell J, Knittel C. (1999). Market Power in Electricity Markets: Beyond Concentration Measures. The Energy Journal 1999; 20(4): 65-88.

12. Mahajan S. Concentration ratios for businesses by industry in 2004. Economic Trends 2006; (635): 25-47.

13. Ballas AA, Fafaliou I. Market shares and concentration in the EU auditing industry: The effects of andersen’s demise. International Advances in Economic Research 2008; 14(4): 485-497.

14. Kozyrev O, Malyzhenkov P. Industrial clusters as the form of the territorial organization of economy in Russia and Italy. European Journal of Economics, Finance And Administrative Sciences - 42 (2011); 133-138.

15. Al-Jarrah IM, Qasrawi W, Obeidat BY, et al. Evaluating the competition and pricing power in the banking sector of Jordan. European Journal of Economics, Finance and Administrative Sciences - 46 (2012); 41-53.

16. Kon A. Economia Industrial. São Paulo: Nobel 1994.

17. Kupfer D, Hasenclever L. (Orgs). Economia industrial: fundamentosteóricos e práticas no Brasil. Rio de Janeiro: Campus 2002.

18. Clarke R. Industrial Economics. Cambridge Massachusetts: Blackwell 1993.

19. Tabner IT. The relationship between concentration and realised volatility: an empirical investigation of the FTSE 100. Doctoral thesis, University of Stirling, Stirling, 2003.

20. Hirschman AO. National Power and the Structure of Foreign Trade. Berkeley:University of California Press 1945.

21. Herfindahl OC. Concentration in the US Steel Industry. Doctoral Dissertation Unpublished, Columbia University, New York 1950.

22. Simpson EH. Measurement of Diversity. Nature 1949; 163(13): 688.

23. Yule GU. Statistical Study of Literary Vocabulary. Cambridge: Cambridge University Press 1944.

24. Fisher RA, Corbet SA, Willams CB. The relation between the number of species and the number of individuals in a random sample of an animal population. Journal of Animal Ecology 1943; 12(1): 42-58.

25. Williams CB. (1946). Yule's Characteristic and the Index of Diversity. Nature 1946; 157(13): 482.

26. Hannah, Kay JA. Concentration in Modern Industry. Theory, Measurement and the U.K. Experience, London: The Macmillan Press 1977.

27. Shannon CE. A mathematical theory of communication. Bell System Technical Journal 1948; 27(3): 379-423.

28. Shannon CE, Weaver W. The mathematical theory of communication urbana and Chicago. University of Illinois Press 1946.

29. Nyquist H. Certain factors affecting telegraph speed. Bell System Technical Journal 1924; 3(4): 324-346.

30. Hartley RVL. Transmission of information. Bell System Technical Journal 1928; 7(4): 535-563.

31. Wiener N. Cybernetics, or control and communication in the animal and the machine. New York and London: MIT

32. Press 1961.

33. Gini C. Memorie di MetodologicaStatistica, Variabilitàe concentrazione. Milano: Dott. A. Giuffrè 1939; 359–408.

34. Lorenz MO. Methods of measuring the concentration of wealth. QuarterlyPublications of the American Statistical Association 1905; 9 (70): 209 –219

35. HMG- Horizontal Merger Guidelines, U.S. Department of Justice and the Federal - Trade Commission, 1997.

36. Fernholz R, Garvy R, Hannon J. Diversity-Weighted Indexing. A New Approach to Passive Investing. The Journal of Portfolio Management 1998; 24(2): 74-82.

37. Goodman LA, Kruskal WH. Measures of association for crossclassification. J. Amer. Statist. Assoc. 1954; 49: 732-764.

38. Rubinfeld R. Robust functional equations and their applications to programtesting. FOCS 1994.

39. Baquero G. Métodos de PesquisaPedagógica. São Paulo: Loyola 1970.

40. Siegel S. Nonparametrics Statistics for the Behavioral Sciences. Tokyo: Kogakusha 1960.

41. Smirnov N. Table for estimating the goodness of fit of empirical distributions. Annals of Mathematical Statistics 1948; 19: 279–281. doi:10.1214/aoms/1177730256

42. Motulsky HJ. Analyzing data with graphPad prism. San Diego (CA): GraphPad Software 1999.

43. Morettin PA, Bussab WO. MétodosQuantitativos (4a ed.). São Paulo: Atual 1991.

DOI: https://doi.org/10.24294/fsj.v5i1.829


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