The data for the analysis was collected from secondary sources. They refer to the 28 members of the European Union. The dependent variable is the level of innovativeness measured by the business sophistication and innovation index (BS&I). The current way of measuring BS&I is available only for the last 8 consecutive periods through the data platform (WEF, 2013). Most of the explanatory variables data was extracted from web-based data platforms (Eurostat, n.d.; Worldbank, n.d.). In addition, data on PP and PPI was collected from the reports of the European Commission on public procurement indicators (EC, 2009, 2012) and from the global competitiveness report database platform (WEF, 2013). European Commission reports do not include data for Croatia (which joined the EU in 2013) and therefore our estimate for Croatia used the annual reports of the Croatian Ministry of Economy (MINGORP, 2013) and data on the Croatian GDP (Worldbank, n.d.). The advantage of secondary sources is the fact that their data collection methodology is known and relies upon a theoretical basis, thus producing comparable data for different countries. The disadvantage of secondary sources is practical field collection of the data by many national organizations, where there may be different interpretations of the otherwise same guidelines. Also, sometimes there are countries that lack data for certain time periods, which creates an unbalanced panel. Even though our data is unbalanced, the Stata software used in the analysis has procedures that correct the estimates for unbalanced data. Although our analysis focuses on the impact of PPI on a country's innovativeness, the emergence of innovation can also be affected by some other conditions, so econometric analysis also has to consider other variables. Therefore, the authors checked additional variables in the extended model: GDP per capita (GDP_pc), GDP growth rate (GDP_gr), gross expenditures on R&D as a percentage of GDP (GERD), government financing share in GERD (G_GERD), share of employees in high-technology and knowledge-intensive activities in the
The data for the analysis was collected from secondary sources. They refer to the 28 members of the European Union. The dependent variable is the level of innovativeness measured by the business sophistication and innovation index (BS&I). The current way of measuring BS&I is available only for the last 8 consecutive periods through the data platform (WEF, 2013). Most of the explanatory variables data was extracted from web-based data platforms (Eurostat, n.d.; Worldbank, n.d.). In addition, data on PP and PPI was collected from the reports of the European Commission on public procurement indicators (EC, 2009, 2012) and from the global competitiveness report database platform (WEF, 2013). European Commission reports do not include data for Croatia (which joined the EU in 2013) and therefore our estimate for Croatia used the annual reports of the Croatian Ministry of Economy (MINGORP, 2013) and data on the Croatian GDP (Worldbank, n.d.). The advantage of secondary sources is the fact that their data collection methodology is known and relies upon a theoretical basis, thus producing comparable data for different countries. The disadvantage of secondary sources is practical field collection of the data by many national organizations, where there may be different interpretations of the otherwise same guidelines. Also, sometimes there are countries that lack data for certain time periods, which creates an unbalanced panel. Even though our data is unbalanced, the Stata software used in the analysis has procedures that correct the estimates for unbalanced data. Although our analysis focuses on the impact of PPI on a country's innovativeness, the emergence of innovation can also be affected by some other conditions, so econometric analysis also has to consider other variables. Therefore, the authors checked additional variables in the extended model: GDP per capita (GDP_pc), GDP growth rate (GDP_gr), gross expenditures on R&D as a percentage of GDP (GERD), government financing share in GERD (G_GERD), share of employees in high-technology and knowledge-intensive activities in the
882259.LexLocalis_Exploration_of_the_effectiveness_of_PPI-corrected_manuscript.docx