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Thoralf Gutierrez UCLouvain Gautier Krings Real Impact, UCLouvain Vincent D Blondel UCLouvain

Indicators of wealth , economic diversity and segregation in Côte d’Ivoire using Mobile Phone datasets. Thoralf Gutierrez UCLouvain Gautier Krings Real Impact, UCLouvain Vincent D Blondel UCLouvain. Côte d’Ivoire. Few reliable statistics on the state of the population

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Thoralf Gutierrez UCLouvain Gautier Krings Real Impact, UCLouvain Vincent D Blondel UCLouvain

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  1. Indicators of wealth, economic diversity and segregationin Côte d’Ivoire using Mobile Phone datasets Thoralf GutierrezUCLouvain Gautier KringsReal Impact, UCLouvain Vincent D BlondelUCLouvain

  2. Côte d’Ivoire Few reliable statistics on the state of the population Civil war that ended two years ago and changed the face of the country

  3. Mobile Phone Datasets • Mobility variables • Where people live • Consumption variables • How people buy airtime credit • Social variables • How people are connected to each other

  4. Mobile Phone Datasets • Mobility variables • Where people live • Consumption variables • How people buy airtime credit • Social variables • How people are connected to each other

  5. Where do people live ? Where are they the most active between 5pm and 5am (at night) ? When they are expected to be at home

  6. Repartition of users Korhogo 1 500 000 150 000 15 000 1 500 Bouaké Daloa Yamoussoukro Gagnoa Abidjan San Pédro

  7. Mobile Phone Datasets • Mobility variables • Where people live • Consumption variables • How people buy airtime credit • Social variables • How people are connected to each other

  8. How do people buy airtime credit ? • Côte d’Ivoire is dominantly prepaid • Do people have a stable behavior ?Do they tend to buy chunks of credit of the same amount ? • 80 % of people have a CV under 60% where is the standard deviation of a person’s top-ups where is the average of a person’s top-ups

  9. Different top-up patterns 1 1 1 1 10 1 1 1 1 1 1

  10. Different top-up patterns Higher household income 10 Averagetop-up size Lower household income 1 1 1 1 1 1 1 1 1 1 Top-up frequency

  11. Average of top-up behavior 0.53 USD 0.73 USD 1.02 USD 1.44 USD

  12. 0.53 USD 0.73 USD 1.02 USD 1.44 USD

  13. Coefficient of Variation of top-up behavior 33.8 % 92.3 % 205.5 % 457.3 %

  14. Mobile Phone Datasets • Mobility variables • Where people live • Consumption variables • How people buy airtime credit • Social variables • How people are connected to each other

  15. Constructing the social graph • Enough communications • Reciprocity • Not especially in developing countries … They are connected if …

  16. Interested in geographically close communities

  17. Coefficient of Variation of top-up behaviorwithin communities 24.9 % 38.5 % 52.1 % 65.8 %

  18. Coefficient of Variation of top-up behavior 33.8 % 92.3 % 205.5 % 457.3 %

  19. Coefficient of Variation of top-up behaviorwithin communities 24.9 % 38.5 % 52.1 % 65.8 %

  20. Mobile Phone Datasets • Mobility variables • Localization of the population within the country • Consumption variables • Estimating wealth, mapping it and its diversity • Social variables • Estimating segregation

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