According to some recent research, the way people buy airtime is a powerful tool for revealing the socio-economic status of developing countries.

When it comes to understanding the social, demographic and economic conditions of a country, the obvious place to look for indicators is the country's most recent census, a national survey carried out to collect just this kind of information. At least, that’s possible in most developed countries.

In most developing countries, census information is much less reliable. In Côte d’Ivoire on the west coast of Africa, for example, the National Statistics Office carried out censuses in 2002 and 2008 but the civil war that broke out during that period makes the results highly unreliable.

Poverty rates in 2002 and 2008 of Cote d’Ivoire

Poverty rates in 2002 and 2008 of Cote d’Ivoire, according to censuses from the National Statistics Office of C ˆ ote ˆ d’Ivoire. (Realisation : Institut National de la Statistique (INS), Division cartographie)

Given this predicament, Thoralf Gutierrez (Universite Catholique de Louvain in Belgium) and some of his friends believe there is a better way to understand the social and economic makeup of a developing country.

With the widespread use of mobile phones in Africa and other developing countries, Gutierrez and company have suggested using mobile phone datasets that record usage habits.

They suggest that the way individuals buy airtime credit is a good indication of their wealth. Also, since mobile phone datasets record the buying habits of a significant proportion of the population, they can reveal the distribution and variation of wealth around a country too.

Gutierrez and company went on to use a dataset of the mobile phone habits of a significant fraction of the population of Côte d’Ivoire which they obtained from one of the country’s large mobile phone operators.

This dataset contains the caller ID and receiver ID for all calls and text messages made in 2012. It identifies the cell tower used and gives each call a timestamp. Crucially, the dataset also contains the timestamp and amount of every airtime credit purchase made by every customer.

The analysis is straightforward. Gutierrez and co. start by analysing the airtime credit purchases and working out by how much each customer varied the amount they purchased. This revealed several different types of customers:

  • Those who made a few big purchases and
  • Others who made many small purchases.

“Our hypothesis is that this difference in behavior predicts household income,” they said.

“Someone who is poor will have to buy airtime credit in small amounts while someone who is rich can make larger purchases.”

Following this, they mapped the average purchases across the country. This map clearly shows the areas where people tend to spend more on airtime credit and are therefore wealthier.

Average of Purchase Averages

Average of each user’s purchase average. (a) Abidjan, (b) Liberian border (c) Roads to Mali and Burkina Faso (d) Road to Ghana

One example is Abidjan, the country’s biggest city and the largest seaport in West Africa.

Another example is the border roads to Mali and Burkina Faso in the north and to Ghana in the south-east. These are economic corridors that are likely to generate wealth. The South Coast is also wealthier, probably because of tourism.

Coefficient of Variation of Purchase averages

Coefficient of Variation of purchase averages, measures
the diversity. (a) Abidjan, (b) Divo (c) Korhogo

The map also throws up some surprising results.

“The Liberian border in the South-West is unexpectedly wealthy,” say Gutierrez and co.

That’s strange because the population density in this area is low and there isn't much industry that can account for any extra wealth. Indeed, the area is known for its insecurity and land conflicts.

There is possibly another explanation according to the researchers. The wealth probably arises from illegal activities on the border, such as drug, arms and human trafficking. Interestingly, that’s not data that an official census would be likely to pick up.

The dataset also reveals areas of inequality which host both rich and poor people. Most urban areas fall into this category however one city, Korhogo, in the north of the country appears to have little inequality for reasons that are not clear.

Gini index of Purchase averages

Gini index of purchase averages, measures inequality.
(a) Abidjan, (b) Divo (c) Korhogo

The Liberian border area mentioned before does not have any diversity either — all the people living there buy airtime in large amounts. Exactly why this is, is unclear too.

Coefficient of Variation of Purchase averages

Weighted average of Coefficient of Variation of average
purchases within each community. (a) Abidjan, (b) Bouake (c) ´
San Pedro (d) Daloa (e) Yamoussoukro

Gutierrez and co. have also studied the social network associated with this dataset. They created a network in which each node is a customer and draw a link between two customers if they communicate at least once per month.

An interesting feature of this network is that people with similar wealth seem to talk to each other.

“People tend to be friends with people that have the same purchase average as themselves,”

A problem with this analysis is that there is no other data to compare it against. That’s a shortcoming that Gutierrez and co. are only too aware of but, given the unreliability of the official census data, there is little they can do to change that.

What is clear, however, is that the study of airtime credit purchase is a powerful tool for understanding the socio-economic status of countries that do not have the resources to conduct large surveys themselves.

There are many possible next steps, try the same technique in other developing countries, to compare the results with reliable ground truth data and to extend the analysis to the developed world, to name just a few.

It’ll be interesting to see where this new science of mobile phone-ology leads next.

Cover Image: Abidjan | Hussein Adallah

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