1 / 23

Relationship between Trade and Technological Competitiveness in Visegrad Group Countries

This research proposal examines the relationship between trade and technological competitiveness in the Visegrad Group countries. It aims to analyze the impact of the technological gap on export competitiveness in Central and Eastern Europe and identify innovation and trade patterns by industry. The paper will use econometric models and estimation results to draw conclusions.

tridder
Download Presentation

Relationship between Trade and Technological Competitiveness in Visegrad Group Countries

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. The Research Proposal of the Relationship between Trade and Technological Competitiveness in the Visegrad Group Countries Marcin Salamaga Kiev 2019

  2. Agenda • motives for taking up the topic and aim of the paper, - researchmethodology, • estimation results of econometric models, • conclusions.

  3. The objectives of the paper • theresearch the direction and strength of the impact the technological gap impact on inequality in export competitiveness in Central and Eastern Europe, • estimation of dynamic panel models describing the dependence of international disparities in the levels of the RCA index on the technological gap, • indication of innovation and trade development patterns by industry.

  4. Innovationintheeconomy • Innovation is the ability to apply the act of creativity to create new ideas, inventions or technological solutions. It is often expressed in finding new combinations of factors of production, adding new value to competitive products and using the achievements of science in the production process. • In the modern world, innovations are the driving force of economic development, and new products, designs, signs and creative projects are crucial in making everyday economic decisions.

  5. Selected theories explaining the foreign trade using technology and innovation • life-cycletheory (Vernon, 1966), • technological gap theory (Posner 1961), • trading models based on the new growth theory (Romer 1992, Grossman-Helpman 1991).

  6. Research methodology used to study the relationship between innovation and foreign trade – references - studies using linear and non-linear regression models based on cross-sectional and panel data (Ghanbari A., Ahmadi M. 2017, Bloom N., Draca M., Reenen van J., 2015 Ezzeddine S., Sami Hammami, M., 2018), HasanovZ., Othman A., Aktamov S,. 2015, Granda I., Fonfría A. 2009), -autoregressionmodels (Keller W., 2009, Milberg W., Houston E. 2005), -gravitymodels(Martínez-Zarzoso I., Márquez-Ramos L., 2015), -VAR and VEC models, (Natera J., M., 2017, Furman J., Porter M., Stern S., 2002; Castellacci F, 2011; Filippetti A., Peyrache A., 2011) -regression models in connection with constructs of innovation indexes (Ezzeddine S., Hammami M.S., 2018)

  7. RevealedComparativeAdvantageIndex - (1) where: Exij – the value of exports from the sector i in the country j, Exj– the total value of exports of the countryj, –thevalue of exports of the sectori in reference countries, – thetotal value of exports in reference countries. Adjusted RevealedComparativeAdvantageIndex (2)

  8. TechnologicalComparative AdvantageIndex - (3) where: Pij – the number of patents from the sector i in the country j, Pj– the total number of patents of the countryj, –the number of patents of the sector i in reference countries, – total number of patents in reference countries. AdjustedTechnologicalComparativeAdvantageIndex (4)

  9. Dynamic panel data model - (5) Xit – thematrix of explanatory variables for the level iin period t, yi,t–thevector of the value of the explained variable for the level iin the period t, yi,t-1–the vector of the value of the explained variable for the level i in the period t-1, β0, β1,φ– model parameters. εit–vector of the value of the error term for the level iin period t. Theestimation by thetwo-step generalized method of moments (GMM) Thebasicequation of model for GMM (6) Estymator (GMM) (7)

  10. The estimation by the two-step generalized method of moments (GMM) (7)

  11. Data for research Data for calculations regarding exports come from the Comext database (Eurostat), and data on patents (patents received in mode of the Patent Cooperation Treaty) - from the database of the World Intellectual Property Organization (WIPO). The time range of the analysis covers the years 2001-2018. A proposal of themeasure of the technological gap and distance in trade competitiveness (8) (9)

  12. Technological and trade inequalities in the fuel and energy industry between the Central and Eastern Europecountries

  13. Technological and trade inequalities in the fuel and energy industry between the Central and Eastern Europecountries

  14. Technological and trade inequalities in the vehicle manufacturing industry industry between the Central and Eastern Europe countries

  15. Technological and trade inequalities in the vehicle manufacturing industry industry between the Central and Eastern Europe countries

  16. Technological and trade inequalities in the agry-foodindustry between the Central and Eastern Europecountries

  17. Technological and trade inequalities in the agry-food industry between the Central and Eastern Europe countries

  18. Estimation results of a dynamic panel data model describing changes in the distance of export competitiveness in highly technologically advanced industries (chemical, pharmaceutical, transport, electronic and precise industries)

  19. Estimation results of a dynamic panel data model describing changes in the distance of export competitiveness in medium technologically advanced industries (fuel and energy, metal, machinery, electromechanical, other products)

  20. Estimation results of a dynamic panel data model describing changes in the distance of export competitiveness in medium technologically advanced industries (mineral, agri-food, wood and paper industry, light industry)

  21. Conclusions • theinnovation and technology generally has a significant and positive impact on the competitiveness of exports in the high and medium-high-tech sectors of Central and Eastern Europe, while it does not significantly affect the competitiveness of trade in the low-tech sectors, • -the technology gap has the strongest impact on the trade distance in high-tech industries, • -at the level of individual industrial branches of the analyzedcountries, the nature of the relationship between technological and trade distance varies depending on the ability of countries to inter-sectoral technological contend.

  22. Conclusions • the obtained results can help to identify those industries in which by reducing the technology gap itis possible to improve trade competitiveness and thus increase trade. Especially where the response of the trade distance to changing the technological gap is strong, it pays to invest in innovation and technology development, • -these issues require further research taking into account other variables used to assess innovation (R&D expenditure, the number of scientific articles per 1 million inhabitants, the number of scientific articles per 1 million inhabitants, employment in high-tech industries and others).

  23. Thank you for your attention

More Related