Blind spots: how a lack of reliable data undermines development

(c)Markel Rodondo, Conakry, Guinea

Despite its critics, vaccines are the most cost-effective and efficient forms of preventing disease. Yet millions of kids die from vaccine-preventable diseases – like pneumonia and measles – every year.

Despite routine vaccination and vaccination campaigns, many developing countries have repeated outbreaks of disease.  In 2013, Médecins Sans Frontières vaccinated 2.5 million people in response to measles outbreaks; half of those were in the Democratic Republic of Congo. (MSF, 2014)

So where do governments and aid agencies go wrong in not getting the vaccines kids need?

A lack of data – in many forms – is crucial. And this is where governments, in a particular, have a responsibility to improve data collection. Many country governments have blind spots when it comes to how many of their nation’s children are properly vaccinated, or even where their people live.

As Kazungu and Adetifa observe:

Good quality vaccination data is required to understand inequities in access to vaccines. Most vaccination coverage estimates in LMICs are from administrative reports that tend to overestimate coverage, due to errors in the number of vaccine doses administered and/or invalid assumptions about the size of the target population of children. (Jacob Kazungu, 2017)

Much of this lies at the foot of governments. Morten Jerven argues – with good reason – that the interest, or more often the lack thereof, in gathering data on their populations’ health, wealth and wisdom is often down to whether its in the best interests of the politicians. As he states:

Data collection is often done in accordance with a governance imperative: statistics are collected and compiled in order to facilitate particular policy agendas. Conversely, a lack of data may signal and facilitate inaction. (Jerven, 2013)

Overestimation of vaccination coverage is unfortunately a common theme throughout many states in Africa. In 2009, Burkina Faso experienced a measles outbreak with over 50,000 cases – to the surprise of many, it turns out. “[the country’s] vaccine coverage estimates preceding this outbreak suggested the country was near elimination, not at risk for a large outbreak.” (Nita Bharti, 2016)

It’s not only a lack of data on vaccination coverage rates that have an impact on disease outbreaks. The ability of a country to be able to provide vaccines for their country’s children in the first place has an impact. The price a country pays for a vaccine is a large part of this. Pharmaceutical companies are notoriously opaque about pricing – and the pneumonia vaccine is a great example of this.

What each country pays for this vaccine is largely a secret. Countries of like-sized economic status – take the BRICS, for example – don’t know what each other is paying for the pneumonia vaccine.  This lack of data “impacts… international or national policy evaluation, and these evaluations in turn have direct implications for issues of governance.” (Jerven, 2013) In this case, whether or not a government can afford to buy a life-saving vaccine that could protect kids against a disease that kills a million children each year.

Médecins Sans Frontières launched a campaign – called A Fair Shot – in part to highlight this lack of data around the price of the pneumonia vaccine. Here’s a short, funny video that explains the part of the campaign where prices are hidden:

The campaign was designed to ask two pharmaceutical companies – Pfizer and GlaxoSmithKline – to reduce the price of the pneumonia vaccine to $5 per child in developing countries. In the early part of the campaign, it highlighted that countries don’t know what other countries are paying for the vaccine, therefore they had to negotiate a price with the companies ‘blind’.

To highlight the issue, and solve the problem, MSF, working with the Guardian newspaper, decided to crowdsource the prices of the pneumonia vaccine in different countries. They asked readers and supporters to visit their local pharmacy or hospital and ask for the price of the vaccine, and then report it to the Guardian or to MSF.

MSF and the Guardian used social media posts to boost this ask:

The request to ask the public to send data on vaccine prices worked. MSF and the Guardian were able to source prices for the pneumonia vaccine from countries and sources that had hitherto been unknown or unavailable.

While these blind spots – or lack of data – in vaccination exist, there are way around them. Governments can do more, “but data availability is a good indicator of political commitment.” (Jerven, 2013) Governments will source the data they require, and respond to the needs accordingly – but only if the political will to do so is there.

 

Works Cited

Jacob Kazungu, I. A. (2017, February 15). Crude childhood vaccination coverage in West Africa: Trends and predictors of completeness. Wellcome Open Research.

Jerven, M. (2013). Poor numbers: how we are misled by African development statistics and what to do about it. Ithaca: Cornell University Press.

MSF. (2014). International Activity Report 2013. Médecins Sans Frontières .

Nita Bharti, A. D. (2016, October). Measuring populations to improve vaccination coverage . Scientific Reports.

 

Big Data from a Feminist Perspective: #HerNetHerRights

Big Data from a Feminist Perspective #HerNetHerRights
Image Source: Google

One of the main topics on our blog is big data and its importance in international development and human development. In my previous posts I had the opportunity to cover the impact of big data on development and the challenges of using big data for humanitarian purposes. And I talked about how big data and new online technologies pose some risks related to privacy and ethics. In other words, how problems from our everyday ‘analogue’ life become real issues in the virtual reality online.

Online violence, especially violence against women and girls, is one of the many serious issues that arise as a consequence of our always-connected world.

There are initiatives and projects that fight against these kind of inequalities that tend to form online. And to analyze the tendencies of online violence against women in Europe, the European Women’s Lobby (EWL) began to lead a project called HerNetHerRights.

What We Need to Know About the #HerNetHerRights Project?

  • Its main purpose is to fight against online violence where women and girls are the victims of male violence.
  • There will be an online conference on October 13th 2017 where activists, researchers and survivors will come together to discuss the current trends and new challenges related to the problem of online violence against women and girls.
  • The sponsor of the #HerNetHerRights project is Google.
Image Source: Twitter - #HerNetHerRights
Image Source: Twitter

The event is part of the annual week-long event called “European Week of Action for Girls 2017”. There will be a discussion on Twitter after the conference. And participants can further comment on the issues reported during the conference.

HerNetHerRights’ conference agenda includes discussions around different forms of online violence, such as:

  • Feminist implications of big data and privacy
  • Analysis of reports on cyber violence against women
  • Sharing experiences from first hand

Big Data and Privacy from a Feminist Perspective

The topic that I’m personally interested in is the one that will be covered by Nicole Shephard. During this event, she will be sharing her experiences with the ‘feminist implications of big data and privacy’ (European Women’s Lobby, 2017) and I personally expect her to also refer to her work called “Big data and sexual surveillance” where Shephard shows the challenges and opportunities that women (and not only) encounter when data, surveillance, gender and sexuality meet together.

In her “5 reasons why surveillance is a feminist issue” Shephard refers to De Lillo (1985) arguing that the “fictional speculation that “you are the sum total of your data” has proven quite visionary” (Shephard, 2017).

In conclusion, big data and the use of technologies for analyzing it don’t seem to be neutral. And they have their own biases. For example, Shephard argues that “racist algorithms” such as Google’s “unprofessional hair” results can be found everywhere in our daily life (Shephard, 2017). And, unfortunately, the end results are not neutral at all. But we should also consider the fact that errors happen and “unprofessional hair” can be as unintentional as “what is the national anthem of Bulgaria”.

References

European Women’s Lobby, 2017, Last Checked: 8/10/2017, Retrieved From: http://www.womenlobby.org/HerNetHerRights

Nicole Shephard, 2016, Big data and sexual surveillance, Last Checked: 8/10/2017, Retrieved From: https://www.apc.org/sites/default/files/BigDataSexualSurveillance_0_0.pdf

Nicole Shephard, 2017, 5 reasons why surveillance is a feminist issue, Last Checked: 8/10/2017, Retrieved From: http://eprints.lse.ac.uk/78521/1/Engenderings%20%E2%80%93%205%20reasons%20why%20surveillance%20is%20a%20feminist%20issue.pdf

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

Data for Data’s Sake?

”Data has become increasingly important to the way we think and talk about conflict and our humanitarian responses to it”, Read, Taithe and Ginty write in their article ”Data hubris? Humanitarian information systems and the mirage of technology” from 2016. They exemplify this by referring to the UN High Level Panel on the Post-2015 Development Agenda’s ”call for a ”data revolution””, which ”would draw on existing and new sources of data to fully integrate statistics into decision making, promote open access to, and use of, data” (UN High Level Panel; Read, Taithe and Ginty (2016, p. 1).

However, ”data is not knowledge”, as the authors of this article emphasize, and they refer to geographer Trevor Barnes’ question: are we generating useful knowledge or are we collecting ”data for data’s sake”? (Read, Taithe, Ginty, 2016, p. 2).

I can relate this very well to my experience from advising newly arrived refugees at my home town in Northern Norway. From the moment when new asylum seekers or refugees arrive in Norway, the different authorities that are involved in the processing of the refugees’ cases will begin to gather data about them and their families. Throughout their asylum process and after they are granted a residence permit, the same data will be gathered again and again because so many different bodies or stakeholders are involved. In our work, therefore, I sometimes question myself if we are collecting ”data for data’s sake”. New technology gives us opportunities to collect, store and manage data in new ways. At the same time, the requirement on data collection, together with applying new (and sometimes hard to implement) technology where the data is stored, has the tendency to be caught up in bureocratic procedures that may make our work much less efficient than it could have been.

In their article, Read et al. conclude that ”the declarations of emancipation via a data revolution are premature” (Read et al, 2016, p. 12). The cases for this conclusion may be different from my case of data gathering about refugees, however the conclusions can be applied here as well. The authors of the article suggest, among other things, an improvement of the data-processing capabilities of humanitarian organisations as well as a request to ”collect enough, but not excessive, information” (p 13). This needs to be taken into consideration in the “data revolution” that is called for by the UN High Level Panel.

This is not intended to be a pessimistic statement against the ICT for development (ICT4D) or the ”datafication” of humanitarian work, but is meant to highlight one of the challenges one is facing when the digital world meets humanitarian work or development practice. New perspectives on this will come in the following blog posts.

References: Roisin Read, Bertrand Taithe & Roger Mac Ginty (2016): Data hubris? Humanitarian information systems and the mirage of technology, Third World Quarterly.

UN High Level Panel, Economies through Sustainable Development.

Big Data and Its Impact on International Development

Big Data and Its Impact on International Development7
Image Source: Google

The term big data is used to describe an enormous volume of data which can be both structured and unstructured. And at the same time, this data is difficult to understand if using conventional information processing techniques (Wikipedia 2017, Big data).

Definitions of big data “vary by industries such as information technology (IT), computer science, marketing, social media, communication, data storage, analytics, and statistics” (Spratt and Baker, 2016, p. 8).

Big Data and Development

In terms of international development, big data provides important contributions in key development areas. These areas include resource management, economic productivity, health care, natural disaster, job market, etc. The analysis of online user-generated data provides opportunities for people from all over the world to have their voice heard.

In their research, Spratt and Baker argue that big data “will be the fuel that drives the next industrial revolution, radically reshaping economic structures, employment patterns and reaching into every aspect of economic and social life” (Spratt and Baker, 2016, p. 4). If in 1946 the first computers weighed thousands of kilograms and could do no more than 500 calculations per second, these days, the IBM Watson supercomputer can process 500 gigabytes per second. That is the equivalent of reading one million books per second.

However, as Spratt and Baker suggest, we should “distinguish big data from two related concepts: information and communications technology (ICT) and ‘open data’” (Spratt and Baker, 2016, p. 8). They argue that “big data is not always open, and at times will not be accessible without special skills or software” (Spratt and Baker, 2016, p. 8).

Big Data and Its Direct Impact on Development

As suggested by Spratt and Baker (2016), the potential impacts of big data can be classified as direct or indirect.

Directly, big data contributes to the process of creating new markets which are based on both production and consumption of data. Inevitably, this leads to the creation of a new complex physical infrastructure that is able to fully support the process of data production and consumption.

The three V’s of big data stimulated innovations in software, including data analysis, data management and networking. This allowed the creation of many now multi billion companies. Such companies had their start from open source projects such as Hadoop which was initially created to store and process huge amounts of data (Spratt and Baker, 2016, p. 8).

Thus, data science becomes a profession and data scientists combine “the skills of software programmer, statistician and storyteller/artist to extract the nuggets of gold hidden under mountains of data” (The Economist, 2010). Such employment opportunities require special qualification. And the demand for this special kind of knowledge creates these new employment opportunities.

In fact, these consequences have a great impact on developing countries. Many corporations outsource their data analysis departments to developing countries where computer skills are high and costs are low. And “skilled young adults in Uruguay will find themselves competing for certain types of jobs against their counterparts in Orange County” (MSNBC 2013). Amazon Mechanical Turk and Samasource (a non-profit business) are some of the organizations that promote the outsourcing of digital work to unemployed people around the world.

Big Data and Privacy

In terms of privacy, the buying and selling of data can create negative impacts as well. Once they collect the data, consumers are not in control of that data (Craig and Ludloff, 2011). And that’s an issue in both developed and developing countries.

Furthermore, issues like privacy and discrimination seem to be working in favor of the digital divide.  In fact, “while data-driven discrimination is advancing at exactly the same pace as data processing technologies, awareness and mechanisms for combating it are not” (Taylor, 2017, p.2). This contributes to various issues like online data privacy, transparency, (technological) inequality… (Wikipedia 2017, Big data). In this regard, Taylor draws a parallel between the idea of justice in general and the idea of data justice. We need data justice to “determine ethical paths through a datafying world” (Taylor 2017, p. 2). Linnet Taylor argues that the importance of big data and datafication and their positive impact on “citizenship, freedom and social justice are minimal in comparison to corporations and states’ ability to use data to intervene and influence” (Taylor, 2017, p. 2).

The Indirect Impact of Big Data

Indirectly, various institutions and sectors will experience the positive influence of the impact of big data. Because it can increase efficiency and productivity. Methods using big data can create organizational improvements of companies, public institutions, NGOs and even social movements. But at the same time, it may negatively impact concerns around privacy and civil rights. And this may lead to increasing social and economic inequalities such as the so called ‘digital divide’.

References