The Importance of Being Counted

The title reminds me of the Norwegian children’s story by Alf Prøysen about the little goat who counted to ten. In the story, the goat begins to count himself, and when he meets his friends he asks if he can count them too. «I don’t think I have the courage, I’m not even sure my mother would let me», says his friend the calf and tries to get away, but the goat counts him anyway: «I am one, you are two.»

The calf starts to cry and calls for his mother, and when the mother cow arrives, the goat counts her, too. «Now he counted you too!» says the calf, and the mother cow becomes furious. The calf counts more and more animals as he is chased around, and in the end they arrive to a river and the goat jumps on to a boat with all the animals after him.

The skipper on the boat panics and cries out: «Does anyone here know how to count? This boat can only take ten animals!» The goat counts all the animals: they are ten, so they are safe. The story ends as all the animals applaude the goat and he becomes the skipper’s helper on the boat.

You might say that this example is a bit silly and childish, but on the other hand it certainly does illustrate both the skepticism towards and the importance of being counted.

Taylor and Schroeder (2014) talk about the importance of being counted (Taylor and Schroeder, 2014, p. 506) when referring to Morten Jerven’s highly interesting book «Poor Numbers» about the lack of accurate data on Africa and in African development work. According to Jerven’s experience and findings, the statistics on African economy are inaccurate, arbitrary and misleading. Consequently, of course, important decisions are being made by actors in African development on the basis of poor numbers.

This illustrates one central example of the relevance of data for development and, more precisely, the importance of gathering accurate (and enough) data to be used in development policy. It illustrates one of the major problems when it comes to data gathering in developing, low- and middle-income countries is that data gathering is poor, or even absent (Taylor and Schroeder, 2014, p. 504).

And why is it important to be counted? We have already answered that question: simply said, because decisions are made and measures are implemented on the basis of the data. For example, counting the population in a country is «vital for the measurement and practise of development» (Jerven, 2013, p. 56). Therefore, being counted also means getting access to resources. (Taylor and Schroeder, 2014, p. 504).

Another example of the importance of being counted is Aadhaar, a biometric ID system database in India. The Aadhaar number is a 12-digit number that Indian citizens receive on the basis of both demographic (name, age, etc.) and biometric (fingerprints, iris scan) information.

Aadhar can be used, citing from the Unique Identification Authority of India’s website: “a basis/primary identifier to roll out several Government welfare schemes and programmes for effective service delivery…” and “is a strategic policy tool for social and financial inclusion, public sector delivery reforms” and so on.

The problem is that not everyone can be identified by their fingerprints or by an iris scan. As pointed out by Taylor (2017), people who do heavy manual work may not have fingerprints and people who are malnourished may not have good enough iris scans (Taylor, 2017, p. 5). Therefore, the Aadhaar system excludes the poorest part of the population.

That being said, according to Taylor, it seems like Aadhaar recognizes the challenges of the system and is working on how to reach more of India’s citizens (ibid).

For Morten Jerven, a solution for poor numbers in African economy is more knowledge and research emphasizing the relevance and quality of data in (African) development. A first step towards better data is certainly made by recognizing the problem. It now remains for researchers in development to pick up the thread.

References:

About Aardaar, from the UIDAI website, retreived from https://uidai.gov.in/your-aadhaar/about-aadhaar.html on October 11th, 2017.

Jerven, M. (2013): Poor Numbers: How We Are Misled By African Development Statistics and What To Do About it. Ithaca, NY: Cornell University Press.

Sandnes (2014), Geitekillingen som kunne telle til ti, Sandnes media, retreived from https://tv.nrk.no/program/msue11004013/geitekillingen-som-kunne-telle-til-ti on October 10th, 2017.

Taylor, L. 2017: What is data justice? The case for connecting digital rights and freedoms on the global level, draft paper.

Taylor, L., Schroeder R. 2015: Is bigger better? The emergence of big data as a tool for international development policy. GeoJournal 80: 503-528.

Data for Development – contributions from the Nordic countries

OECD is one of the international institutions that makes use of data produced by our use of technology, such as mobile phones and internet, in their research and policymaking (Taylor and Schroeder, 2014, p. 505).

At the end of this month, on October 30th, OECD will release its Development Co-operation Report for 2017, Data for Development. In the introduction of the report, OECD stresses the importance of big data and the data revolution for development in a broad sense: “more and better data can help boost inclusive growth, fight inequalities and combat climate change. These data are also essential to measure and monitor progress against the Sustainable Development Goals.”

Furthermore, the report stresses the fact that the lack of good data is still evident in most developing countries, and asks central questions: Why are over half of deaths and one-third of births worldwide unaccounted for? Why is investment in statistical capacity (…) not a priority for most providers of development assistance?

The report calls for a strengthening of national statistical systems and will present measures to “make data work for development”.

The profiles of different countries’ contributions to data for development is already published. This post briefly looks at the contributions from the Nordic countries in the report: Denmark, Norway, Sweden and Finland.

Denmark
According to the report, more and better data is getting more important than earlier for Denmark when it comes to reporting on the Sustainable Development Goals (SDGs). Until now, it has not been a high priority. Denmark aims to “improve statistical production and promote the use of data by policy makers, civil society and citizens”.

During 2013-2015, Denmark committed on average 12,7 million USD per year to finance statistical capacities and systems in developing countries.

Norway
Norway contributes to strenghtening data for development through its national statistical institute, Statistics Norway. Among other things, Statistics Norway collaborates with the UN’s High Commissioner for Refugees “to establish international guidelines for collecting and producing statistics on refugees and internally displaced people.”

Norway aims to “improve statistical production and data literacy and to strengthen co-ordination among development partners” and it contributes in helping developing countries “to build up civil registration and vital statistics”.

This year, Statistics Norway participated in an expert panel meeting where it provided input to the Development Co-operation report, stressing the importance of building holistic and sustainable national statistical systems (p. 10 in Statistic Norway’s newsletter “International Development Cooperation at Statistics Norway”, June 2017).

Norway launched a white paper this year in where it stresses the importance of good data and statistics in development cooperation.

Norway committed on average 15,0 million USD per year in 2013-15 to finance statistical capacity building.

Countries where Statistics Norway has Institutional Cooperation:

Source: Statistic Norway’s newsletter, June 2017

Sweden
Data for development or building up statistical capacities is an important part of Sweden’s development work. It is included in Sweden’s Budget Bill and the new policy framework for Sweden’s development co-operation. Sweden contributes in improving statistical production and literacy in development countries and offers technical assistance and financial aid. Among other things, it gives financial support to the UN Global Pulse, “which harnesses big data for development and humanitarian action”.

Interestingly, Statistics Sweden has collaborated with Burkina Faso and Mali on establishing “continuous household surveys” to help building up statistical capacity.

Sweden committed on average 20,89 million USD per year to finance statistical capacity in 2013-15.

Finland
The OECD report does not contain any information on Finland’s contribution to data for development. Finland’s contribution this field therefore remains unanswered.

However, Finland contributed a much smaller amount of money than its Nordic neighbours (1,51 million USD per year in 2013-15) to statistical capacity building, which indicates that it is not a priority.

The full country profiles are available here: http://dx.doi.org/10.1787/dcr-2017-en

References:

OECD (2017) Development Co-operation Report 2017: Data for Development, OECD Publishing, Paris. Retrieved 2nd October 2017 from http://dx.doi.org/10.1787/dcr-2017-en

Statistisk sentralbyrå (2017): International Development Cooperation at Statistics Norway: A newsletter from Statistics Norway’s Division for Development Cooperation, June 2017. Retrieved 2nd October 2017 from
https://www.ssb.no/omssb/samarbeid/internasjonalt-utviklingssamarbeid/_attachment/316124?_ts=15d36b4d518

Taylor, L., Schroeder R. (2015): Is bigger better? The emergence of big data as a tool for international development policy. GeoJournal 80: 503-528.