What are the characteristics of innovative economies and what are the factors that make some countries more innovative?
Author
Grodzinska, Karolina Ewa
Term
4. term
Publication year
2019
Pages
100
Abstract
Dette speciale undersøger, hvad der kendetegner innovative økonomier, og hvorfor nogle lande er mere innovative end andre, ved at analysere data fra Global Innovation Index (GII), en udbredt rangliste over landes innovationspræstationer. Arbejdet gennemgår centrale teorier om innovation både som begreb og som system og introducerer National Systems of Innovation – de netværk af institutioner, regler og politikker, der former innovation i et land. Specialet beskriver også, hvordan innovation og konkurrenceevne måles, hvad disse mål faktisk fanger, og hvor de kommer til kort, og gør eksplicit rede for begrænsninger i både data og tilgang. Den kvantitative del anvender usuperviseret maskinlæring – nærmere bestemt hierarkisk klyngedannelse – til at gruppere økonomier med lignende profiler uden foruddefinerede kategorier. Diskussionen og konklusionen advarer mod at tolke samlede indikatorer for bredt og retter kritik mod GII’s mål for innovativitet. Fordi GII kombinerer mange forhold, der ikke direkte handler om innovation, er det ikke muligt entydigt at sige, hvad der gør nogle lande mere innovative. Alligevel kan GII bruges til at få indsigt på makroøkonomisk niveau, sammenligne lande ud fra flere kriterier og identificere styrker, barrierer og muligheder for innovation.
This thesis examines what characterizes innovative economies and why some countries are more innovative than others by analyzing data from the Global Innovation Index (GII), a widely used ranking of countries’ innovation performance. It reviews core theories of innovation both as a concept and as a system, introducing National Systems of Innovation—the networks of institutions, rules, and policies that shape innovation within a country. The thesis also explains how innovation and competitiveness are measured, what these metrics do and do not capture, and it clearly states the limitations of the data and the approach. The quantitative analysis uses unsupervised machine learning—specifically hierarchical clustering—to group economies with similar profiles without predefined labels. The discussion and conclusion urge caution when interpreting aggregate indicators and critique the GII’s measures of innovativeness. Because the GII framework combines many factors not directly tied to innovation, we cannot make a single, definitive claim about what makes some countries more innovative. Even so, the GII is useful for macro-level insight, benchmarking countries against multiple criteria, and identifying strengths, obstacles, and opportunities for innovation.
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