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Does financial development help to align growth opportunities with growth? Evidence from industry-level data


Financial institutions are expected to play a crucial role in reallocating resources in favor of industries facing greater global and local shocks to growth opportunities. Fisman and Love, in their paper entitled “Financial development and intersectoral allocation: A new approach”, argue that growth opportunities are unobservable and propose a new methodology to test the capital allocation hypothesis. The methodology is based on correlations in the patterns of intra-industry growth between two countries and similarities in the level of financial development and income. This paper extends their methodology by proposing direct and forward-looking measures of local and global growth opportunities, obtained by interacting the country’s patterns of industrial specialization with industry-level price-earnings ratios, as in the paper “Global growth opportunities and market integration” by Bekaert et al. The results, obtained in a cross-section framework including 37 developed and developing countries over the period 1992–2006, confirm the relevance of financial development to promote economic growth and to help industries in taking advantage of global and local growth opportunities. They also show that the methodology developed by Fisman and Love can be extended to include direct measures of growth opportunities.

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  1. 1.

    Theoretical studies support this idea and argue that well-developed financial systems, by reducing the asymmetric information between the borrower and the lender (Greenwood and Jovanovic 1990; Bencivenga and Smith 1993; King and Levine 1993) and providing risk management services (Levine 1991; Saint-Paul 1992), can devote resources to the most productive uses and stimulate economic growth. For a deeper understanding of the literature on financial development and economic growth, see Levine (2005) and Pietrovito (2012).

  2. 2.

    In general, financial development is only one of the country-specific characteristics helping industries to capture shocks to growth opportunities. Other relevant factors are, for example, the level of human capital and the institutional quality.

  3. 3.

    The existing literature on corporate finance and on finance and growth also proposes other proxies for growth opportunities, such as Tobin’s Q [see, for instance, Pilotte (1992) and Erickson and Whited (2000)] and sales growth [see, for instance, Fisman and Love (2004b)].

  4. 4.

    Following FL, the test focuses on two separate assumptions about the structure of shocks to growth opportunities across countries: the first is that global shocks affect a given industry equally across all countries; the second is that shocks are country-specific and affect all the industries in a country equally. This is the reason why in specification (3) global and local growth opportunities are included alternatively.

  5. 5.

    For the relationship between FDI and growth, see, for instance, Borensztein et al. (1998), Alfaro et al. (2009) and Cipollina et al. (2012).

  6. 6.

    Using this methodology allows to exploit the panel dimension of the data in estimating the matrix of correlations among growth rates of value added across sectors and countries and among country and sector specific characteristics. These correlations are then used in a second stage cross-section regression.

  7. 7.

    FL, to deal with this issue, adopt the quadratic assignment procedure (QAP) to calculate the significance level. The “QAP is in essence a bootstrap procedure that preserves interdependencies between rows and column” (Fisman and Love 2004a, p. 2791). A different solution would be to estimate an OLS regression and adjust standard errors clustering by pair of countries. This procedure treats only observations including different pairs of countries as independent. In adopting this solution, our results, available on request, remain unchanged.

  8. 8.

    Table 1 provides descriptions and sources of all the variables used in the empirical analysis.

  9. 9.

    Countries included in the analysis are: Argentina, Australia, Austria, Belgium, Canada, Chile, Colombia, Cyprus, the Czech Republic, Denmark, Finland, France, Germany, Greece, Hungary, India, Israel, Italy, Japan, Korea, Luxembourg, Malaysia, Mexico, the Netherlands, New Zealand, Norway, Peru, the Philippines, Portugal, South Africa, Spain, Sweden, Switzerland, Thailand, Turkey, the United Kingdom and the United States.

  10. 10.

    Even though Indstat4 2008—Revision 3 includes data on more than 100 countries, the number of countries included in the present study is determined by the availability of data on price-earnings ratios. Moreover, the decision to keep 1992 as the initial year depends on the fact that, in this year, data on price-earnings ratios are available for all the countries included in the sample.

  11. 11.

    Datastream adopts the Industry Classification Benchmark (ICB) to classify all the economic activities. More specifically, it uses 39 industrial sectors (level 3 in Datastream) and 104 sub-sectors (level 4 in Datastream).

  12. 12.

    The data on price-earnings ratios and value added have been reconciled according to the scheme adopted in Bekaert et al. (2007).

  13. 13.

    Moreover, these indicators exclude the credit issued to governments and public agencies and the credit issued by the central bank.

  14. 14.

    It should be noticed that the distance in GDP is used in the paper by FL as an indirect indicator of similarities in local growth opportunities, whereas in this paper it is used as a control variable.

  15. 15.

    Data on FDI for Luxembourg are referred to 2002 which is the first year available.

  16. 16.

    FDI is defined as an investment involving a long-term relationship and reflecting a lasting interest in and control by a resident entity in one economy (foreign direct investor or parent enterprise) of an enterprise resident in a different economy (FDI enterprise or affiliate enterprise or foreign affiliate). Such investment involves both the initial transaction between the two entities and all subsequent transactions between them and among foreign affiliates.

  17. 17.

    Since the bootstrap procedure calculates normal-based confidence intervals (i.e. confidence intervals based on the normality assumption of the distribution), we also calculate percentile and biased corrected confidence intervals [for a theoretical comparison, see Hall (1988)]. The two methods give similar results, meaning that the observed value of the standard error is equal to the median of the bootstrap distribution. Moreover, the bootstrap distribution is approximately normal since these methods give similar confidence intervals and the number of replications (1,000) is large enough. Results are available on request.


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This paper is based on a portion of my dissertation at the University of Molise. First of all, I would like to thank Alberto Franco Pozzolo for his valuable supervision and support. I would also like to acknowledge the comments of the members of my thesis committee: Giovanni Ferri, Sergio Ginebri, and Francesco Nucci. I would like to thank an anonymous referee for providing useful comments. I am also grateful to the Electronic Resources Area of Bocconi University’s library for providing me with access to the Worldscope Database, and to Dario Focarelli, Stephan Siegel and Paolo Zanghieri. Any remaining errors are my own.

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Correspondence to Filomena Pietrovito.

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Pietrovito, F. Does financial development help to align growth opportunities with growth? Evidence from industry-level data. Rev World Econ 150, 421–442 (2014).

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  • Capital allocation
  • Financial development
  • Global growth opportunities
  • Local growth opportunities
  • Industry-level

JEL Classification

  • G1
  • G2
  • O1