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Abstract

In this chapter, the author (Eric Rougier) first argues that real-life competition generally consists of complex systems of legal regulations, firms’ strategies and public policies, and cannot be expressed as simple scores on a product market liberalization scale. The author then shows that types of product market governance are differentiated along two main lines: the degree of product market regulation (red tape, business climate, taxes), and the style of state interventionism, involving either traditional directive industrial and trade policies, or policies focused on Foreign Direct Investment (FDI) attraction and short-run integration into world value chains. The countries are consequently distributed into five different types: the liberalized deregulated, export-oriented, statist partially liberalized, statist protected and idiosyncratic. The chapter ends with a discussion of those models.

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Notes

  1. 1.

    Product market deregulation, insofar as it triggers competition for incumbent firms, is widely seen as a key determinant of output and productivity growth in both developed (Nicoletti and Scarpetta 2003; Blanchard and Giavazzi 2003; Wölfl et al. 2009) and developing economies (Djankov et al. 2002, 2006; Loayza et al. 2004, 2005).

  2. 2.

    Ever since Schumpeter (1934), it has been widely accepted that competition has two contradictory but complementary effects on growth. On the one hand, increased competition has an adverse effect, by eroding the rents of the innovative firms, whose monopolistic position may be contested by potential entrants. On the other hand, competition and entry also have a positive impact on innovation, since they produce strong incentives for incumbent firms to find new products or to reduce their costs so as to temporarily escape competition.

  3. 3.

    This point has received empirical support by Acemoglu et al. (2006) on the basis of cross-sectional aggregated data. Amable et al. (2010), who tested the assumption of a non-linear competition effect on productivity using sector-based data, have been less supportive of this point.

  4. 4.

    Except for Amable (2003), CC does not always make a clear and systematic distinction between the different domains pertaining to the production domain, broadly defined by Soskice (1999: 101) as “the organization of production through markets and market-related institutions”. Accordingly, it articulates such dimensions as industrial and labour relations, competition policies, as well as the financial system (Hall and Soskice 2001).

  5. 5.

    They averaged, for 1986–1995, five International Country Risk Guide scores assessing the government’s role in protecting against private diversion: (i) law and order, (ii) bureaucratic quality, (iii) corruption, (iv) risk of expropriation, and (v) government repudiation of contracts.

  6. 6.

    In recent empirical literature, competition is mainly assessed by such outcome measurements as entry rates on domestic markets (Hoekman et al. 2001; Aghion et al. 2005). Sector-based margins of incumbent firms are often used to measure competition intensity, assuming that more entry decreases incumbents’ rents. Such measurements are not, however, relevant at cross-national level. Moreover, outcome measurements cannot describe the full set of policies and the range of competition system varieties.

  7. 7.

    However imperfect, Freedom House types of economic organization and other indicators, such as the share of the output provided by state-owned enterprises (World Bank Development Indicators), or the Government effectiveness and Regulatory quality scores of the World Bank Governance Index, will be used in Table 6.4 as characterization variables of our clusters.

  8. 8.

    It should be noted that the ex-post use of these variables does not affect the PCA.

  9. 9.

    Active countries’ projection can be found in the Appendix, Fig. 6.5.

  10. 10.

    By construction, the optimal number of components needed to account for data variability is determined by (i) the proportion of total variance explained by each component, (ii) the absolute variance explained by each component (the Eigen value of each component retained should exceed value one) and (iii) the capacity of each component to be interpreted meaningfully.

  11. 11.

    This correlation is not surprising, since recent empirical studies have shown that corruption provides entrepreneurs with more flexibility in over-regulated environments (MĂ©on and Weill 2010).

  12. 12.

    See Piveteau and Rougier (2011) for an analysis of this shift in industrial policy from traditional developmental state directive policies towards FDI-attraction policies.

  13. 13.

    The relevant partition, i.e., the relevant number of clusters, is derived from the dendrogram analysis and the analysis of two indicators which respectively measure (i) the improvement of the inter- to intra-cluster variance ratio when one moves from a given partition to another and (ii) the impact of k-means consolidation on that ratio.

  14. 14.

    More accurately, the standardized Euclidian distance between these countries and the barycentre is less than half the median distance.

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Appendix

Appendix

Table 6.5 Variables used in the cluster analysis
Table 6.6 Correlation matrix
Table 6.7 Summary statistics (means and standard deviation)
Fig. 6.5
figure 5

Country projection over the second and third components. Data source: Author’s calculations

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Rougier, E. (2017). Product Market and Competition. In: Rougier, E., Combarnous, F. (eds) The Diversity of Emerging Capitalisms in Developing Countries. Palgrave Macmillan, Cham. https://doi.org/10.1007/978-3-319-49947-5_6

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