Abstract
This analysis is aimed at identifying the main determinants of international competitiveness of an economy and at designing measures thereof and verifying a hypothesis concerning the diversification of influence of individual determinants with respect to groups of countries singled out on the basis of characteristic features of their innovation systems. The issue of identification of the above mentioned determinants and diversification of their influence is of high importance in terms of verification of efficiency and purposefulness of allocation of resources among fixed assets, human capital, and research and development (R&D)—and thus, for both the purposes of assessing the economic policy pursued and designing future development strategies.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
See Chap. 1.
- 2.
see also Reiljan and Kulu (2002).
- 3.
The quality-of-life aspect is an important part of many definitions of competitiveness, see e.g., Aiginger (1998, 2006a, 2006b) and Czajkowski (2010a).
- 4.
See Czajkowski (2010b, pp. 73, formula 15).
- 5.
Various proxy benchmarks of ULCs and labor productivity will be used as ULCC,t.
- 6.
The indexes listed are the simplest intermediate benchmarks. This issue is discussed in more detail in Chap. 2.
- 7.
See Czajkowski (2010b).
- 8.
This topic is discussed in more detail in Chap. 3.
- 9.
Data published by OECD did not include a major part of the studied countries, while other databases—e.g., EU KLEMS— cover too limited scope of years.
- 10.
Eurostat data: Hourly labour costs – Nace Rev. 1.1 [lc_an_costh], Bureau of Labour Statistics (BLS) series Hourly compensation costs in manufacturing, U.S. dollars, 1996–2010.
- 11.
Barro–Lee series were used: Average years of tertiary schooling, age 15 + , total, available under signature BAR.TER.SCHL.15UP in the World Bank data base (http://databank.worldbank.org/ddp/home.do data collection for Education Statistics).
- 12.
Barro–Lee series Average years of total schooling, age 15 + , total, series signature BAR.SCHL.15UP source as above.
- 13.
To obtain P-values, rcorr function of Hmisc library of R package was used.
- 14.
Sometimes, instead of α i +  τ t , the following notation is applied: α i, t .
- 15.
The term individual effects is in the literature used interchangeably with group effects in the literature of the subject.
- 16.
α  +  τ is reduced in this model to α ’≡ α .
- 17.
To implement the Chow test, pooltest function from PLM library of R package was used. See op.cit. Croissant, Millo (2008), pp. 20.
- 18.
pFtest function from PLM library of R package was used, see Croissant, Millo (2008), pp. 21.
- 19.
In this study t = 16, which is an intermediate value between short and long series.
- 20.
More specifically: the existence of serial correlation in the idiosyncratic error component.
- 21.
More specifically: the existence of AR(1) or MA(1) processes in the idiosyncratic error component.
- 22.
Time series cross section, TSCS.
- 23.
The estimator is available in Imtest library of R package, through of vcovHC function, see Croissant, Millo (2008), pp. 31.
- 24.
The implementation in R package is discussed in Bailey, Katz (2011).
- 25.
H0 of the Wald test has the following form: ∀(i) β i  = 0.
- 26.
In this case, due to the manner of construction of S c, t and the interpretation of the logarithm of this variable, this is admissible; see comment to formula (4.17).
- 27.
See Maddala, 1992, pp. 269–295.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Appendix
Appendix
1.1 Model estimates in accordance with test results per effect type (see Table 4.5 and 4.11), error values and significance levels before adjustment
Printout 1. Model T.1.1—with random individual effects, estimation with the Amemiya estimator, prior to the adjustment of errors in and significance levels of parameter estimations. (signif. codes for all printouts: *** p < 0.001; ** p < 0.01; * p < 0.05; . p < 0.1)
Printout 2. Model T.1.2—with random bidirectional effects, estimation with the Amemiya estimator, prior to the adjustment of errors in and significance levels of parameter estimations.
Printout 3. Model T.2.1—with random bidirectional effects, estimation with the Amemiya estimator, prior to the adjustment of errors in and significance levels of parameter estimations.
Printout 4. Model T.2.2—without random or constant individual or time (pooled) effects, prior to the adjustment of errors in and significance levels of parameter estimations.
1.2 Estimation of parameters by the use of the PCSE model
The estimated PCSE models were modified in relation to the models estimated above to correspond to them in the best possible way.
Printout 5. Model T.1.1. In order to take into account the individual effects, a PCSE model was estimated with binary variables for countries (an equivalent of the fixed-effects model with individual effects; it is not possible to obtain an equivalent of the random effects model with the use of PCSE).
Printout 6. Model T.1.2. In order to take into account the individual effects, a PCSE model was estimated with binary variables for countries and years (an equivalent of the bidirectional fixed-effects model; it is not possible to obtain an equivalent of the random effects model with the use of PCSE).
Printout 7. T.2.1. In order to take into account the individual effects, a PCSE model was estimated with binary variables for countries and years (an equivalent of the bidirectional fixed-effects model; it is not possible to obtain an equivalent of the random-effects model with the use of PCSE).
Printout 8. T.2.2. A model without binary variables (an equivalent of the pooled model).
1.3 Dispersion charts for ln(Sc) in relation to natural logarithms of independent variables per groups
Group T.1.1. (after including the Netherlands and elimination of independent variable M(c, t), see Table. 4.4; source: own work)
Group T.1.2. (Source: own work)
Group T.2.1. (Source: own work)
Group T.2.2. (Source: own work)
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this chapter
Cite this chapter
Czajkowski, Z. (2014). Human Capital and Innovations As Determinants of Competitiveness. In: Weresa, M. (eds) Innovation, Human Capital and Trade Competitiveness. Innovation, Technology, and Knowledge Management. Springer, Cham. https://doi.org/10.1007/978-3-319-02072-3_4
Download citation
DOI: https://doi.org/10.1007/978-3-319-02072-3_4
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-02071-6
Online ISBN: 978-3-319-02072-3
eBook Packages: Business and EconomicsBusiness and Management (R0)