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Global dynamics, capabilities and the crisis

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Abstract

The financial crisis started in 2007–8, initially in the US, but its consequences have been felt throughout the global economy. However, its effects were far from uniform. While parts of Asia and Africa continued to grow fast, Europe experienced a large set back. This paper emphasizes three important factors: differences across countries in technological development; differences in capacities to exploit the opportunities offered by technology; and differences in the ability to compete in international market. A formal model, based on this approach, is developed and applied to data for 100 countries in the period 1997–2012. Empirical indicators reflecting the various factors are developed, a dataset constructed and econometric estimates of the model performed. The results are used to explore the factors behind the slowdown in economic growth, with a particular emphasis on the continuing stagnation in Europe. A major factor turns out to be the increased financialization of the economy. The negative effect of the growth of finance prior to the crisis is especially pronounced for the countries that suffered most during the crisis.

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Notes

  1. 1.

    However, while this earlier work assumed strictly balanced trade, the model presented below allows for deviations from this rule.

  2. 2.

    This may occur through adjustments of the fiscal and monetary policy stance, but it may also be the result of the working of markets, such as the capital, labor and currency markets.

  3. 3.

    See Appendix 4 for details on how Eq. (6) was derived.

  4. 4.

    As can be seen from Eq. (6), the expected sign of the effects of changing relative prices on growth depends on whether or not the so-called Marshall-Lerner condition is satisfied.

  5. 5.

    We hold it as unlikely that changes in a country’s technological capability and social capacity can be seen as mere reflections of its rate of economic growth. A stronger case may exist for an effect of economic growth on price growth, since the price-level by definition is a relation between the value and quantity of what is produced. However, the largest share of value added consists of wages, which often are determined through negotiations of various sorts, and subject to influence by institutions, politics etc., which we in the present context have chosen to consider as exogenous.

  6. 6.

    In principle, this increases the possibility for reverse causation. Arguably, most countries are too small to have a significant influence on world demand. Nevertheless, there may be a few countries among the one hundred taken into account here for which this assumption can be questioned, and we will test for the sensitivity of the estimates to this.

  7. 7.

    Missing observations were estimated using the impute procedure in Stata 11.2, for more information see Stata (2005, pp. 217–221). The procedure, which is regression-based, uses information from other variables in the data set to fill in missing values. This applies to the following cases (% of estimated observations in brackets): R&D expenditures (11 %); gross tertiary enrolment (1 %); quality of bureaucracy (9 %), freedom from corruption (1 %) and external debt (10 %).

  8. 8.

    If necessary unity was added to avoid logs of zero.

  9. 9.

    See Fagerberg (1994) for an overview and discussion.

  10. 10.

    Both merchandise trade and trade in services are included. While merchandise trade is used at 3-digit level of SITC, rev. 3, with 255 product categories, the available data on trade in services only allow us to distinguish three service categories (transport, travel and other services).

  11. 11.

    Several other potentially relevant control variables were tested for possible inclusion in the model. However, as the estimated coefficients did not come out anywhere close to being significant at conventional levels, they were not retained in the model. This includes the size of government (general government final consumption expenditure as % of GDP), income inequality as measured by the Gini index, access to ocean or navigable rivers, Köppen–Geiger ecozones, Holdridge life zones and the composition of religious adherence.

  12. 12.

    Beta values are reported, i.e. the variables enter the analysis with mean of zero and standard deviation of one, thus the estimated coefficients refer to the impact of change by one standard deviation.

  13. 13.

    Results from these additional tests are available from the authors on request.

  14. 14.

    Arcand et al. (2015) suggest that the effect of financial development (F), measured in different ways, on economic growth should be modelled as F = a1 S + a2 S2, where S is an indicator of the size of the financial sector. However, according to the model developed in this paper, it is the growth of financial capability, not its initial level, that should be expected to affect subsequent economic growth, and this leads to a different specification. Note that, by totally differentiating F we get dF = a1 dS + 2 a2 S dS, i.e., the two terms included in the model here.

  15. 15.

    We also tested for a possible change in the impact of the interaction terms ((Δ finance × finance) and (Δ trade balance × external debt)) during the crisis; however, this hypothesis was not supported.

  16. 16.

    See Fagerberg and Verspagen (2015) for a more in-depth discussion of this issue.

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Acknowledgments

Financial support from the VINNOVA Core Funding of Centers for Innovation Systems (project 2010–01370 on ‘Transformation and Growth in Innovation Systems: Innovation Policy for Global Competitiveness of SMEs and R&I Milieus’), Czech Science Foundation (project P402/10/2310 on ‘Innovation, productivity and policy: What can we learn from micro data?’) and Czech Academy of Sciences (institutional support RVO 67985998 and agenda ‘Strategie AV21’) are gratefully acknowledged. Earlier versions of the paper were presented at the 15th ISS Conference, 27–30 July, 2014, Jena, Germany, the 2013 Eu-SPRI Forum Conference on Management of Innovation Policies, 10–12 April, 2013, Madrid, Spain and the Joint UNU-MERIT/School of Governance Seminar, 6 June 2013, Maastricht, Netherlands.

Author information

Correspondence to Martin Srholec.

Appendix

Appendix

Table 7

Table 7 Regional groups of countries

Table 8

Table 8 Control variables

Table 9

Table 9 Testing for differences in variable impact between the pre-crisis period of 2002–2007 and the crisis period of 2007–2012 (pooled OLS)

From (3):

$$ x-m=b-p $$
(9)

From (1) – (2):

$$ x={\varepsilon}_{XT}t+{\varepsilon}_{XC}c+{\varepsilon}_{XP}p+{\varepsilon}_{XW}w $$
(10)
$$ m=-{\varepsilon}_{MT}t-{\varepsilon}_{MC}c-{\varepsilon}_{MP}p+{\varepsilon}_{MY}y $$
(11)

By subtracting (11) from (10):

$$ x-m=\left({\varepsilon}_{XT}+{\varepsilon}_{MT}\right)t+\left({\varepsilon}_{XC}+{\varepsilon}_{MC}\right)c+\left({\varepsilon}_{XP}+{\varepsilon}_{MP}\right)p+{\varepsilon}_{XW}w-{\varepsilon}_{MY}y $$
(12)

From (9) and (12) follows, by eliminating x − m:

$$ b-p=\left({\varepsilon}_{XT}+{\varepsilon}_{MT}\right)t+\left({\varepsilon}_{XC}+{\varepsilon}_{MC}\right)c+\left({\varepsilon}_{XP}+{\varepsilon}_{MP}\right)p+{\varepsilon}_{XW}w-{\varepsilon}_{MY}y $$

By solving for y:

$$ y=\frac{\varepsilon_{XT}+{\varepsilon}_{MT}}{\varepsilon_{MY}}t+\frac{\varepsilon_{XC}+{\varepsilon}_{MC}}{\varepsilon_{MY}}c+\frac{\varepsilon_{XP}+{\varepsilon}_{MP}+1}{\varepsilon_{MY}}p-\frac{1}{\varepsilon_{MY}}b+\frac{\varepsilon_{XW}}{\varepsilon_{MY}}w $$
(13)

From (4) – (5)

$$ t={\varepsilon}_{TN}n+{\varepsilon}_{TD}d $$
$$ t=\gamma {\varepsilon}_{TD}-\gamma {\varepsilon}_{TD}{T}^{gap}+{\varepsilon}_{TN}n $$
(14)

By substituting (14) into (13):

$$ y=\gamma {\varepsilon}_{TD}\frac{\varepsilon_{XT}+{\varepsilon}_{MT}}{\varepsilon_{MY}}-\gamma {\varepsilon}_{TD}\frac{\varepsilon_{XT}+{\varepsilon}_{MT}}{\varepsilon_{MY}}{T}^{gap}+{\varepsilon}_{TN}\frac{\varepsilon_{XT}+{\varepsilon}_{MT}}{\varepsilon_{MY}}n+\frac{\varepsilon_{XC}+{\varepsilon}_{MC}}{\varepsilon_{MY}}c+\frac{\varepsilon_{XP}+{\varepsilon}_{MP}+1}{\varepsilon_{MY}}p-\frac{1}{\varepsilon_{MY}}b+\frac{\varepsilon_{XW}}{\varepsilon_{MY}}w $$

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Fagerberg, J., Srholec, M. Global dynamics, capabilities and the crisis. J Evol Econ 26, 765–784 (2016). https://doi.org/10.1007/s00191-016-0453-9

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Keywords

  • Technological capabilities
  • social capabilities
  • competitiveness
  • economic growth
  • crisis

JEL Classification

  • E11
  • F43
  • O30