CORRELATION BETWEEN REFUGEE POPULATION AND ODA ALLOCATION

WORLD

By Gulsum Dogan
refugee population, official development assistance, foreign aid

This project aims to show the correlation between refugee population and official development assistance.

Starting with Syrian civil war in 2011, Turkey has started to host the highest number of refugees in the world. Most refugees coming from Turkey and other various countries aim to use Turkey as a transit country which would lead them to the countries that are part of the European Union. Unfortunately, as the European Union tried to implement many exclusionary humanitarian practices including Search and Rescue groups to stop the flow of refugees coming through sea or sitting on a table with Turkey in the Refugee Deal to stop the flow of people. There are about 80 million people that are forced to leave their homes in the world and that includes 26 million refugees. The displacement of these people causes indirect or direct effects on the hosting states. To eliminate those effects, it is argued that aid is sometimes based on the political agenda in order to stop the flow of people rather than the need.

ODA is considered one of the most significant foreign aid in the global political agenda. The maps between 2010-2019 aim to show the ODA allocation in order to highlight the amount of aid and its relation to the countries that host the refugee populations. This project is born out of the idea of European Union’s effort to stop the Syrian refugee flow by providing foreign aid to specific countries as briefly mentioned above.

Official Development Assistance(ODA), according to OECD, is defined as ‘government aid designed to promote the economic development and welfare of developing countries. Loans and credits for military purposes are excluded. Aid may be provided bilaterally, from donor to recipient, or channelled through a multilateral development agency such as the United Nations or the World Bank.’ (OECD, iLibrary)

I’ve created multiple maps to show the correlation in each year starting from 2010 to 2019. The reason it ended in 2019 was the unavailability of the ODA data in 2020. Also, the starting point was related to the outbreak of the Syrian Civil War. I obtained my data from UNHCR and World Bank databases which followed a thorough cleaning process and joining two datas based on year. One of the reasons for that was the existence of unrelated fields(including years) on the CVS file, especially for the ODA data. Also, ODA was classified as not only based on specific countries but also as geographical locations such as Central America. (as seen below) In order to match up that data, I needed to clean it up. More importantly, some of the country names were different in UNHCR data and the World Bank Data such as Bolivia or Venezuela. I need to go over each country to match the names.