This past semester, the IPD Health Team has been working on a Senegalese aid analysis project designed to encourage other countries to make similar types of information—such as birthrates, pregnancy rates, and aid distribution to various population groups—available to AidData. Using ArcGIS maps, data visualizations, and written analyses, our goal was to demonstrate how data can be used for more effective aid distribution and potentially improve the standard of living within a country. One of the most important findings, however, was that the direction our analysis took was driven by our lack of, rather than abundance of, data. Accordingly, we didn’t merely demonstrate the importance of data, but also the impact that missing data can have on attempts to analyze trends in aid.
Our original plan was to create five one-page documents, each with a different health indicator mapped alongside World Bank or other health aid projects. These indicators included pregnancies, births, water stress, age structures, and ethnic group distribution; however, inaccessibility to age structure and water stress data limited our analyses in these areas. Most of the data that we used, provided by AidData, was easily displayed on maps and thorough enough for statistical and spatial analysis. The health aid projects, for example, each included at least ten pieces of information including project start date, donor, location, and total funding committed. Some of the data that we pulled from other sources—such as WorldPop’s pregnancy and birth data—integrated seamlessly with the information provided by AidData. Other data, however—such as the water stress data from RiverThreat.net—made it much more difficult to create detailed maps.
Figure 1: Water Stressed Areas of Senegal, source: RiverThreat.org
The only water stress data available to us was a color-coded map of the entire world. As you can see, a zoomed-in view of Senegal on this map provides low-resolution, unitless data with little value for analysis. Similarly, WorldPop claimed to provide age structure data for Senegal, but in its place we only found a map containing population density information. The lack of data for these two indicators prevented us from creating two of our five desired maps and left many of our questions unanswered. These gaps in data also reduced the impact and power of the maps that we were able to create, leaving us with maps and analyses depicting fairly intuitive patterns.
FIGURE 2: Aid Distribution and Pregnancies in Senegal, Source: IPD Global Health Team
For example, one of the more obvious takeaways from our pregnancy distribution analysis was that pregnancies are concentrated around Dakar, the capital of the country. One could reach the same conclusion by doing a quick search of Senegal on Google or Wikipedia, where the area surrounding Dakar is shown to be the most densely populated region in the country. While data from the rest of the country provided valuable insight on the more rural regions, where population information is not as widely available, we may have been able to provide more illuminating analyses if the water stress or age structure information had been accessible. Some examples of questions that we were unable to answer due to data limitations include:
We created three one-page documents using the information that was available to us, but we knew that more complete, though currently unavailable, data would have enhanced our ability to answer more interesting questions.
Access to every possible statistic about a country would obviously be ideal, but this is an impractical wish. So how do we deal with this lack of data? Do we submit to it, or do we stand up to it? For this project, we submitted; we did our very best with the data that was available to us. But it doesn’t always have to be this way! Promoting accessibility and transparency of foreign aid data is a primary goal of IPD, and the necessity of such improvements grew increasingly apparent throughout our project.
By the end of this analysis, I had developed a newfound understanding of the types of problems that any group working with development data must deal with. When is the available data adequate? When do we search for more available data? When do we collect new data? Each research project demands a different answer. However, increased accessibility and transparency of data would address this shortcoming in every case.
Jonathan Walsh is an undergraduate studying biomedical engineering at the University of Texas at Austin.
Traditional economic growth models rest on the concept of rational economic actors making the most personally profitable transaction. Of course, no one is perfectly rational. These models merely represent and describe a perceived reality. In real life, external or intangible factors often lead to decision-making biases and non-rational behavior. Behavioral economics demonstrate that even the most fundamental microeconomic models do not accurately reflect the situations faced by the extreme poor.
These situations often demand overwhelming decisions, from rationing the family’s food, to finding money for rent. Distraction, anxiety, and fatigue are more likely when the basic necessities of life are variable and uncertain. The mental and physiological effects of poverty can create a vicious cycle. Even a minor “cognitive tax” can impede one’s ability to make a rational decision that might mitigate poverty, whether that is making low-risk, long-term investments or seeking preventative medical care. Obviously, this phenomenon complicates aid projects designed to serve society’s poorest members.
Consider a financial inclusion program aimed at the “bottom of the pyramid.” Beneficiaries may feel uncomfortable when brought into the unfamiliar environment of a bank to discuss opening an account. Additionally, beneficiaries must read unfamiliar legal language to decide what type of account they need. These discomforts are forms of cognitive taxes. People in such situations are also more likely to consider advertising or packaging when making their decisions, without fully understanding the consequences. Factors like the physical accessibility of finance centers, the emotional connection with workers, and the simplicity of the preferable actions may matter much more to an individual than perceived profitability.
In most instances the people designing such a financial awareness program are middle-to-upper class and exempt from the same financial decisions, since operating a bank account is often assumed for them, rather than being a question to consider. My point here is not that policymakers are ignorant of the challenges facing the poor, or that the poor are focusing on the wrong aspects of decision-making. Rather, aid projects are more likely to succeed when the program design acknowledges that beneficiaries often have other things on their mind than the future profits of possible financial investments.
In many ways, these minute economic decisions reflect the context of economic structure. The informal sector contributes about 55% of Sub-Saharan Africa’s GDP and about 80% of the labor force. Since many developing countries lack strong formal institutions, people often rely on social norms of the informal sector as the basis for their economic activity. So it follows that if those norms promote cooperative and collective behavior, higher levels of trust will emerge. Studies have shown that higher rates of trust and reciprocity within a society are associated with a higher rate of economic growth. Additional research into how policymakers can encourage those cooperative social preferences might open new opportunities for economic growth.
But conjecture into the attitudes of the poor is simply not a sound foundation for aid policy. The development field’s emerging norms of “data-driven decisions” should include the quantifiable influences of poverty on economic behavior. Recent behavioral experiments have adopted this approach. For example, they have explored the influence of poverty on the propensity to cooperate in social dilemmas; trust and reciprocity; norms of fairness and altruism; and risk and time preferences. As a next step, research should explore how these intangible social norms translate into tangible economic activities.
Here, too, there is opportunity for collaboration and trust-building through transparency. Data-sharing relationships between behavioral economists and policymakers could lead to smarter, more efficient program designs that lift more people out of poverty.
Laura Holzenkamp is a Sophomore majoring in Economics and International Relations & Global Studies.
Image source: http://schwadroncartoons.com/
Data literacy is the ability to read and analyze data as an effective means of communication. It is a skill that can be used to assess the large-scale data of corporations or to read a graph comparing a country’s health and education statistics. Data is used across every discipline and every line of work, yet many people don’t know how to use or read it. Failure to interpret data correctly can lead policy makers or business executives to overreact or underreact to situations represented by data. These mistakes can be costly and may divert funds from areas where they are direly needed to areas where they are less essential. An important component of data literacy includes considering how data is collected, who collects it, and how it is made available to the public. When overlooking these factors, an interpreter might only consider the data directly in front of him/her instead of the underlying information behind each data point.
On August 29, 2014 UN Secretary-General Ban Ki-moon appealed for the creation of data tools that would reinforce national and global statistical transparency and efficacy. As a part of this initiative the Secretary-General appointed an independent expert advisory group to lead the UN on a Data Revolution for Sustainable Development. Among measures addressing data innovation and the data landscape, part of this initiative focuses on improving data literacy around the world. According to undatarevolution.org, activities to improve data literacy need to emphasize data accessibility by establishing regulations and motivating data owners to open their data to the public. Additionally, the initiative seeks to improve how and when data is collected. Data literacy is necessary so that people can hold governments, intergovernmental agencies, and nonprofits accountable for development outcomes, such as health, education, and regional infrastructure projects.
While data is vital to understanding progress and development at a subnational, national, regional, and global level, we as undergraduate students and new members of IPD’s Climate Change Team, have been daunted by data and how to interpret it. Hundreds of indicators, password-protected files, and the need for technical savvy have often made data difficult to analyze and interpret for untrained researchers.
Our experiences at IPD have exposed us to tools and software that can turn data into more easily readable charts and maps. However, we have only learned about the existence of such tools because we sought them out in an educational environment. Most people around the world do not have this opportunity. As emphasized by the Secretary-General, there is a need to find better ways to read and access the seemingly endless amounts of data available.
The UN’s Data Revolution for Sustainable Development echoes Swedish Professor Hans Rosling’s platform for data literacy, gapminder.org. Started in 2005, Gapminder is a project furthering the UN’s Millennium Development Goals and sustainable global development. Coinciding with Rosling’s aim to heighten the practice of understanding statistics from a fact-based worldview, Gapminder features tools for unpacking data through graphs and maps.
Gapminder World converts complex data on countries into adjustable and easy to use maps. The site features Professor Rosling explaining and exploring data sets and trends. The site also has resources for educators, such as lesson plans, handouts, interactive presentations and Gapminder World Offline-- software allowing for continuous updates and access to Gapminder World for those without Internet access.
From our own experiences and knowledge about the lack of data literacy around the world, we understand that interpreting statistical data is a skill not widely developed. Yet data and the ability to interpret it relates to many significant topics in the world, such as interpreting government elections, assessing disease outbreaks, and even calculating one’s own finances. Instead of relying on data, however, people sometimes allow their personal experiences to influence their understanding of the world. This can lead to a distorted and biased view of what’s really going on. To help assess the gaps between those who can read data and those who cannot, Gapminder created The Ignorance Project. The Ignorance Project conducts surveys to uncover gaps in the public’s knowledge of data. These findings can then be used to create better tools that help people with data literacy.
The UN initiative stresses the importance of data literacy for sustainable development. Being able to read and access data allows for participation, idea formation and increased awareness. While having access to good data is extremely important (as the UN Data Revolution points out), it is equally as important to have people who are able to read it. Through data literacy people can have the ability to hold their leaders accountable or make decisions like what school or hospitals are the best. Data literacy, though potentially daunting, has the ability to empower and improve people across the globe.
UN Secretary-General Ban Ki-moon and Professor Rosling have recognized the danger behind ignorance and the benefits of having a data literate world. The existence of data literacy and accessibility tools have the potential to open the door of participation, freedom and responsibility for everyone by explaining difficult information on a basic level. Thanks to sites like Gapminder and the UN’s Data Revolution for Sustainable Development, we are a few steps closer to a data literate world -- and we'll all be the better for it.
Juliet Carrillo is a Sophomore, undeclared, and Shadhi Mansoori is a Freshman majoring in Neuroscience.
Image source: https://www.flickr.com/photos/gforsythe/8508041553/