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Financial System and Technological Catching-up: an Empirical Analysis

Is there a Recipe for Increasing the Export Variety of Nations?

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Long Term Economic Development

Part of the book series: Economic Complexity and Evolution ((ECAE))

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Abstract

This paper explores the role of the financial system in technological catching-up in the expectation that financing mechanisms affect the production and the exports of new or “new to the market” commodities. We have developed indices of related export variety (REV) and of unrelated export variety (UEV) by using the informational entropy function for a sample of 97 countries using NBER & UN trade data for the period 1992–2005. We used these indices sequentially as dependent variables with the bank credit ratio and stock market capitalization ratio as independent variables. In addition, we include the education system, natural resources and four principal component factors characterizing the cost of doing business, political system, quality of governance and the degree of openness of the countries as control variables in our regressions. Our pooled regression models show that the financial system is an important determinant of both types of export variety for all countries but that, for the most successful developers, the banking system and the stock market play different roles, with the former being relatively more appropriate for REV and the latter for UEV. Such specialization of different forms of the financial system seems to confirm that stock markets are likely to be relatively more appropriate to fund the exploratory type of innovations which are required to increase UEV.

Reprinted from Evolutionary Economics 22(4), 847-870 , Springer (2012)

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Notes

  1. 1.

    Rajan and Zingales (1998) while studying financing pattern of US firms declare that drugs and medicines (ISIC 3522) industries are the most dependent, while the tobacco industry (ISIC 314) is least dependent on external finance.

  2. 2.

    A firm, interested in issuing a security on the stock exchange, is required to submit a registration statement to the Securities and Exchange Commission, which includes information about the proposed financing, the firm’s history, existing business and future plans.

  3. 3.

    We used the mean and standard deviation of the pooled data for the standardization, which implies that the change of a composite variable over time will reflect both changes in each country’s position (relative to other countries) and changes in the importance of the underlying indicators over time. (See Adelman and Morris 1965, 1967)

  4. 4.

    To avoid bias against large economies, both variables were regressed against (the log of) land area and the residuals from these regressions were then used in factor analysis. (See Fagerberg and Srholec 2008)

  5. 5.

    The Education Index is measured by the adult literacy rate (with two-thirds weighting) and the combined primary, secondary, and tertiary gross enrolment ratio (with one-third weighting).

  6. 6.

    The oil dummy equals one for countries designated as oil-exporting by the IMF and zero otherwise.

  7. 7.

    Quantile Regressions are used when the effects of the independent variables vary across the level of dependent variable. As in our case, export verities have very dissimilar pattern across the different nations.

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Acknowledgements

Earlier versions of this paper were presented at Thematic Meeting of the French Economic Association “Firms, Markets and Innovation” Sophia Antipolis, France, June 25–26, 2009 and 13th Conference of the International Joseph A. Schumpeter Society, Aalborg University, Denmark, 21–24 June 2010. We wish to thank the participants and discussants at these events. Authors also owe gratitude to Flora Bellone, Lionel Nesta and the editors and referees of this journal for their useful comments on the earlier versions of this paper. All usual caveats apply.

Author information

Authors and Affiliations

  1. Karachi School for Business & Leadership, ST-03, Dawood Co-operative Housing Society, Bahadurabad, National Stadium Road, Karachi, Pakistan

    Muhammad Nadeem Javaid

  2. INRA-GAEL, Université Pierre Mendès-France, 47, 38040, Grenoble, France

    Pier-Paolo Saviotti

  3. GREDEG-CNRS, 250 rue Albert Einstein, 06560, Valbonne, France

    Pier-Paolo Saviotti

Authors
  1. Muhammad Nadeem Javaid
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  2. Pier-Paolo Saviotti
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Correspondence to Muhammad Nadeem Javaid .

Editor information

Editors and Affiliations

  1. , Lehrstuhl für Innovationsökonomik, University of Hohenheim, Wollgrasweg 23, Stuttgart, 70593, Germany

    Andreas Pyka

  2. , Department of Business and Management, Aalborg University, Fibigerstraede 16, Aalborg East, 9220, Denmark

    Esben Sloth Andersen

Appendix

Appendix

Table 4 Summary statistics
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Table 5 Correlation matrix (figures in parenthesis are p values)
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Table 6 List of the countries
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Javaid, M.N., Saviotti, PP. (2013). Financial System and Technological Catching-up: an Empirical Analysis. In: Pyka, A., Andersen, E. (eds) Long Term Economic Development. Economic Complexity and Evolution. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35125-9_21

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