We like statistics!

Nordwit recently had its two-day Spring meeting, through zoom of course. One of our topics was statistics and particularly statistics on gender and gender (in)equality in research and innovation.

We all agreed that we like – or even love – statistics. The academic world is full of statistics that we produce and use in many ways. The world outside the academy –  politicians, media, experts in public and private sector organizations –  eagerly wants to have statistics to be able to name facts. Statistics and numbers are found equivalent to factual knowledge social phenomena including gender inequalities. Thus, the statistical figures represent an image of a hard and true knowledge. People rely on facts, and the possibility to present numerical facts provides convincing truths. However, during our discussion it also appeared that statistics are far from easy to find, and even more difficult to interpret in the analysis of gender inequalities.

Statistics around gender inequalities are produced both in national institutions such as Statistics Finland, ministries, labor unions, Technology Industries of Finland, and supranational institutions such as OECD, ILO and European Commission. The numbers presented in the statistics derive from various sources. These sources also include a variety of definitions and guidelines according to which the raw data is classified. My close colleague Merja Kinnunen (1997) analysed already twenty years ago how gender was embedded in statistical classifications in Finland. She showed that classifications are institutionalized texts, which are also a means to manage and control the material world. The classifications include cultural perceptions, symbols and images and at the same time they describe structural features of society. Thus it is worth remembering that the statistical descriptions also shape, maintain and legitimate the existing structures.

Currently supranational statistics such as She Figures, a key source for an analysis of gender questions, are based on other supranational databases such as Eurostat. However, the assumption is that these originally national sources, are equivalent and they measure the same issues in each society. This has needed a lot of standardization and negotiation. It has been necessary because equivalent information provides better chances to make comparisons between the countries. Still, it is relevant also to ask the same questions that Merja Kinnunen (2001) posed: How do the classifications and statistics as institutional texts participate in the legitimization of the differences and hierarchies between women and men in society? As statistics is highly standardized across the (Western) countries, it is also relevant to ask, what kind of reality do the standardized numbers tell and what kind of reality remains hidden behind the figures?

Päivi Korvajärvi


Kinnunen, Merja (1997) “Making Gender with Classifications.” In: Rantalaiho, Liisa & Heiskanen, Tuula (eds) Gendered Practices in Working Life. London: Macmillan, 37-51.

Kinnunen, Merja (2001) Luokiteltu sukupuoli [Classified Gender]. Tampere: Vastapaino.

She Figures 2018 (2019) European Commission, Directorate-General for Research and Innovation. Luxembourg: Publication Office of the European Union.

What motivates girls to choose a career in technology?

(Photo: NHO)

Studies of young people’s motivation to pursue a career in technology have often focused on when and how interest in technology develops. Many teenagers lose interest in science and technology, and because his affects girls more than boys, it leaves a short gap to capture girls’ interest, it has been argued. Many initiatives to increase girls’ interest have been designed based on images of boys’ interest in video gaming and programming. The problem is that this type of interest is also gendered.

We are in the process of concluding a survey among girls in Norway with nearly 700 respondents who were studying science and technology at high schools and universities.

What has been the most important motivation for your choice of studying in science and technology?

When we asked the girls this question, the top 9 motivating factors were all related to working life and society:

  • 93% agreed that exciting job opportunities in technology was an important motivation
  • 80% were strongly motivated by the possibility of using technology for solving social issues.

In the opposite end of the scale we found activities associated with boys:

  • less than 5% of the girls have been motivated through after-school/leisure time activities involving technology
  • less than 14% found video games motivating for choosing technology at high school or university.

These findings support our previous empirical research finding that many girls are motivated by other things than technology when they enter tech education.

The report (in Norwegian) will be out soon, for those who want to read more!


Corneliussen, H.G. (2020) “Dette har jeg aldri gjort før, så dette er jeg sikkert skikkelig flink på” – Rapport om kvinner i IKT og IKT-sikkerhet, Sogndal: VF-rapport 8/2020.
Corneliussen, H.G. (2020) ‘What Brings Women to Cybersecurity? A Qualitative Study of Women’s Pathways to Cybersecurity in Norway’ European Interdisciplinary Cybersecurity Conference (Eicc 2020).
Talks, I., Edvinsson, I., & Birchall, J. (2019). Programmed Out: The gender gap in technology in Scandinavia. Oslo: Plan International Norway.
McKinsey & Company and Pivotal Ventures. (2018). Rebooting representation – using CSR and philanthropy to close the gender gap in tech. https://www.rebootrepresentation.org/report-highlights/: Tech Report 2018 [Accessed March 2021].
Microsoft Corporation. (2017). Why Europe’s Girls Aren’t Studying STEM. – Microsoft Philanthropies.