The faces behind the numbers

This post appeared in Dutch on the Oikocredit Netherlands website. 

Today is Blog Action Day. On this day, people around the world post blogs to draw attention to a designated theme. This year: inequality, a topic that I focus on in my research.

I travelled to Bolivia two years ago. During my stay I interviewed individuals about their experience with inequality. I was perfectly prepared. I had carefully drawn diagrams representing different income distributions on my computer. Identical rectangles pieced together into a pyramid or an hourglass. The questions – thanks to my language course – were translated to Spanish. And, of course, I had read all the relevant academic papers. I was ready.

My host family in Tarija, a small town close to the Argentinian border, warmly welcomed me. I went to a wedding, celebrated New Year’s Eve, and got acquainted with all the aunts and uncles. The culture shock that I had expected didn’t come. The heels were a bit too high for my Dutch feet and the language was more melodious than my own. But with Teresa, the host family’s daughter, I chatted as I would with my friends at home. We shared experiences, enjoyed the same food, and our jeans looked alike.

Shock

The biggest shock I experienced in those first days was that I sometimes felt relatively poor. Like when Jorge – member of the local Rotary Club – showed me a new Santa Cruz neighbourhood. Ten years ago it had been a deserted area, now it was filled with villas, fancy cars, and high fences. Or the time that I was invited for a lunch in an old colonial house filled with antique furniture. When we had finished our starters, my host rang a copper bell. A second later, the two helps entered to clean the table and serve the main dish.

The fifty-eight year old woman Venita broke my bubble. Equipped with a stack of questionnaires, I had arrived at the office of Mujeres en Acción. This organisation aims to empower women that work as household help. Since recently this occupational group was legally entitled to the minimum wage. However, in practice little had changed.

Venita was shorter than me by twenty centimeters. She was dressed in a gray sweater and a worn pair of jeans. “How happy are you on a scale from one to ten?” “One” she answered. She looked fragile and I wondered whether I should just leave her alone. “My employer pays me 200 bolivianos (23 euro) per month. I don’t dare to ask for more. They will probably fire me if I do. And I have no other place to go. I am too old. I live outside in a small tent.”

Rich and poor

“Next, I am going to ask you some questions about the distribution of money between rich and poor in Bolivia” I continued. I glanced at the abstract diagrams I had designed in The Netherlands. Venita could neither read nor write. I cursed at myself. How was she supposed to understand these collections of rectangles? But before I could ask my first question, she started to talk. “Let me tell you how Bolivia works. There are many poor people and very few rich. There is nothing in between.” She straightened her back. “And it only gets worse. People are not to be trusted, let alone the government. Many words, but no actions.” She looked at me with her dark eyes. I bit my pen and flipped through the questionnaire. Venita had just, without realising, answered two pages of questions.

I sometimes think of her when I am gazing at averages, standard deviations or regression coefficients. Because Venita hides behind these numbers. Or Jorge. Or Teresa. People who sometimes taught me more than a dozen academic papers. People who gave faces to abstract concepts. People who answered questions that I could not have thought of. It is their voice that I want my research to echo. 

How Nigeria became twice as rich overnight

This post appeared in Dutch on the Oikocredit website.

One Sunday in April, something remarkable happened: all of a sudden Nigeria became almost twice as rich. The Nigerian statistical agency had recalculated the official statistics and the GDP turned out to be 89% higher than previously thought. Overnight, Nigeria had become the largest African economy, surpassing South Africa.

www.photocontestinsider.com/2013/04/winners-of-the-malaria-photo-contest-announced

Had someone messed with the numbers? After all, companies and other parties have an interest in a larger economy. For years, South Africa had been the African country to invest in; this revision could divert investors’ attention from South Africa to Nigeria. However, economists seem to agree that not the new, but the old statistics were unreliable.

The old statistics could not be trusted because Nigeria was using severely outdated methods for its national accounting (read a more detailed explanation here). This is not just a Nigerian problem: Morten Jerven writes in his book  Poor Numbers that African statistical agencies generally lack people and knowledge, causing them to publish inaccurate numbers. It is not only economic statistics that are often of low quality in Africa, but also other types of data. For example, education and health statistics are often incorrect.

Why should we care about good data? Isn’t that a problem for number fetishists in rich countries? Aren’t there more urgent problems in Africa? It’s probably not the sexiest topic, but data quality is essential for poverty reduction and development. Without good data it is impossible to know where money should be invested, how health care is doing, and what the impact is of a new education policy. Without good data, new policy measures are merely shots in the dark.

Claire Melamed mentions the example of malaria. Malaria is one of the foremost causes of death in poor countries. Nevertheless, good data is scarce. To fight the disease effectively it is important to know which areas are confronted with malaria, how many patients are there, and what is the quality of local healthcare facilities. Moreover, the results of eradication programs can show what works and what doesn’t. For example, should mosquito nets be free or is it better if people pay for them? (Answer: hand them out for free.) Such knowledge doesn’t fall from the sky: local organisations report figures or special teams collect the information needed. This costs money, but such investments have high returns: less people fall ill, less people die.

So how can data quality be improved? In order to figure that out, it is key to understand why statistics are unreliable (and sometimes completely absent) in African countries. First of all, there is a lack of resources. Jerven tells about a visit to the Zambian statistical bureau, where one man is responsible for the entire national accounting. “What happens if I disappear?” he wonders. (To contrast, ninety-nine employees work at the national accounting department of the Dutch statistical agency.) Secondly, perverse incentives sometimes encourage organisations to report wrong numbers. A recent article by Justin Sandefur and Amanda Glassman shows that governments exaggerate the number of vaccinated children in order to receive more donor money. Or schools report higher enrollment figures, because the administration pays them per student. 

Data quality can improve by tackling these two causes. There are initiatives, such as Paris21, that focus on enhancing ‘statistical capacity’ of developing countries by giving money and training. However, that doesn’t solve the problem of wrong incentives, which requires fundamental changes in the way development progress is rewarded.

On that Sunday in April Nigeria showed that it is indeed possible to produce better data. Of course, better data is a not an end in itself. The poor man in a Lagos slum didn’t see his income double overnight. Nor did the ill woman in the countryside all of a sudden have access to a better hospital. Only if good statistics are actually used for better policies, can they make a difference.