Context

In 2013 alone, international migrants sent $413 billion home to families and friends. This money is known as "remittance money", and the total is more than three times that afforded by total global foreign aid ($135 billion). Remittances are traditionally associated with poor migrants moving outside of their home country to find work, supporting their families back home on their foreign wages; as a result, they make up a significant part of the economic picture for many developing countries in the world.

This dataset, published by the World Bank, provides estimates of 2016 remittance movements between various countries. It also provides historical data on the flow of such money going back to 1970.

For a look at how remittances play into the global economy, watch "The hidden force in global economics: sending money home".

Content

This dataset contains three files:

• bilateral-remittance.csv --- Estimated remittances between world countries in the year 2016.
• remittance-inflow.csv --- Historical remittance money inflow into world countries since 1970. Typically high in developing nations.
• remittance-outflow.csv --- Historical remittance money outflow from world countries since 1970. Typically high in more developed nations.

All monetary values are in terms of millions of US dollars.

For more information on how this data was generated and calculated, refer to the World Bank Remittance Data FAQ.

Acknowledgements

This dataset is a republished version of three of the tables published by the World Bank which has been slightly cleaned up for use on Kaggle. For the original source, and other complimentary materials, check out the dataset home page.

Inspiration

• What is the historical trend in remittance inflows and outflows for various countries? How does this relate to the developmental character of the countries in question?
• What countries send to most money abroad? What countries receive the most money from abroad? Try combining this dataset with a demographics dataset to see what countries are most and least reliant on income from abroad.
• How far do workers migrate for a job? Are they staying near home, or going half the world away? Are there any surprising facts about who send money to who?

This comprehensive dataset presents the global refugee landscape by providing a detailed overview of refugee and displacement statistics from various countries and territories over a span of time. With a total of 107,980 rows and 11 columns, this dataset delves into the complexities of forced migration and human displacement, offering insights into the movements of refugees, asylum-seekers, internally displaced persons (IDPs), returned refugees and IDPs, stateless individuals, and other populations of concern.

Columns in the dataset:

• Year: The year of data collection.
• Country / territory of asylum/residence: The host country or territory where refugees seek asylum or residence.
• Origin: The country of origin from which refugees are fleeing.
• Refugees (incl. refugee-like situations): The number of refugees, including those in refugee-like situations.
• Asylum-seekers (pending cases): The count of individuals seeking asylum whose cases are pending.
• Returned refugees: The number of refugees who have returned to their country of origin.
• Internally displaced persons (IDPs): The count of people who have been displaced within their own country due to conflict or other reasons.
• Returned IDPs: The number of internally displaced persons who have returned to their previous locations.
• Stateless persons: Individuals who do not have the legal recognition of any country.
• Others of concern: Additional populations in need of humanitarian assistance.
• Total Population: The sum of all above categories, representing the overall population affected by displacement. The dataset serves as a valuable resource for studying global refugee trends, assessing the impact of conflicts and crises on displacement, and understanding the challenges faced by vulnerable populations worldwide.

Visualization Ideas: Time Series Analysis: Plot the trends in different refugee populations over the years, such as refugees, asylum-seekers, IDPs, returned refugees, etc. Geographic Analysis: Create heatmaps or choropleth maps to visualize refugee flows between different countries and regions. Origin and Destination Analysis: Show the top countries of origin and the top host countries for refugees using bar charts. Pie Charts: Visualize the distribution of different refugee populations (refugees, asylum-seekers, IDPs, etc.) as a percentage of the total population. Stacked Area Chart: Display the cumulative total of different refugee populations over time to observe changes and trends.

Data Modeling and Machine Learning Ideas: Time Series Forecasting: Use machine learning algorithms like ARIMA or LSTM to predict future refugee trends based on historical data. Clustering: Group countries based on similar refugee patterns using clustering algorithms such as K-Means or DBSCAN. Classification: Build a classification model to predict whether a country will experience a significant increase in refugee inflow based on historical and socio-political factors. Sentiment Analysis: Analyze social media or news data to determine the sentiment around refugee-related topics and how it correlates with migration patterns. Network Analysis: Construct a network graph to visualize the connections and interactions between countries in terms of refugee flows.

These visualization and modeling ideas can provide meaningful insights into the global refugee crisis and aid in decision-making, policy formulation, and humanitarian efforts.

Governments in high income countries allocate funding for Official Development Assistance (ODA), and population-based surveys tend to show support for the concept of affluent nations assisting the development of poorer regions. A public opinion survey was conducted in Hong Kong to: (1) assess public support for foreign aid for social development and Hong Kong's current Disaster Relief Fund (DRF); and (2) assess how much respondents thought should be contributed to foreign aid for social development and/or DRF. Interviewers conducted a random telephone survey of Cantonese-speaking Hong Kong citizens aged 18 or above during 2017. Of the 1004 individuals surveyed, 55% (552) agreed that a portion of the government budget should be allocated to the DRF and 37% (372) disagreed. The mean and the median amount of the government budget suggested to be allocated were 5.1% and 2.4% respectively. However only 16% (164) supported the government giving foreign aid for social development, with 79% (793) not supporting, and 5% (47) undecided. The suggested portions of government budget that should be allocated for this purpose were 1.5% (mean) and 0.0% (median). The degree of support for DRF and foreign aid for social development was associated with both age (DRF P < 0.0005; foreign aid for social development P < 0.0005) and education (DRF P = 0.010; foreign aid for social development: P < 0.0005). There was little support for foreign aid for social development amongst the Hong Kong public, in contrast to similar surveys in other countries, but this could be related to the lack of a local tradition of providing ODA to foreign countries. Most respondents supported the current DRF and would like to see a greater proportion of government budget allocated.

We test the hypothesis that aid recipient governments are given greater discretion in distributing aid geographically for personal benefits during periods when they are non-permanent members of the United Nations Security Council (UNSC). More specifically, we analyze whether World Bank projects are targeted to regions in which the head of state was born, or to regions dominated by the same ethnic group as that of the head of the state. We find that all regions of a country on average receive more aid projects during UNSC membership years, confirming previous results with updated data. We find no evidence for additional World Bank projects going to leaders' birth regions during UNSC years. Turning to co-ethnic regions, we find that these regions receive fewer projects in normal times but during times of UNSC membership they receive significantly more projects and greater overall commitments. This effect is driven by loans from the IBRD arm of the World Bank. Importantly, looking at voting patterns in UNSC, we find a much stronger effect when focusing on countries that always vote in line with the US. This gives further credit to the interpretation of the result as a trade of favors between governments in donor and recipient countries catering towards domestic audiences.

The novel coronavirus pandemic has unsettled the political, economic and social structures of the world. Yet, in the context of global economies in recession, opportunities also abound for many countries, including in Africa, to pursue new directions in governance and management. For instance, the pandemic may be closing gaps between the so-called developed and the developing worlds, thereby giving African countries some geopolitical and economic leverage, both in terms of international alliances and managing fiscal challenges. This project, using the case of Sierra Leonne, focuses on how African countries can chart new paths is their management and governance of foreign aid. The project investigates how aid-funded projects are implemented in Africa using the yardstick of the World Bank’s International Good Governance Standard and, in the process, answers the question of how African countries can alternatively and efficiently administer and manage foreign aid-funded projects? This question is important because Sub-Saharan Africa is one of the world’s most aided regions. Aid as a percentage of Gross Domestic Product (GDP) in the region has averaged around 5% for much of the past two decades. Aid has reached nearly 10% at times and still equals nearly 6% of the region’s GDP. Yet, the growth records of nearly all African countries have thus far been unsatisfactory compared with the amount of aid funds received. The case of high aid flows into African economies, on one hand, and evidence of abysmal growth outcomes, on the other, have led to questions about the usefulness of foreign aid. At a time when Africa’s traditional donor countries are biting the dust, due to the pandemic, these questions become even more crucial. The project calls for a rethink of Africa’s economic management practices to meet the needs of present times.

(UNCLASSIFIED) Liberia’s infrastructure has been in shambles for decades, mostly due to two recent civil wars and regional instability. Vital services including disaster relief and an operating health care system are nonfunctioning, causing natural disasters and epidemics to be even more dangerous. Intense summer rains often lead to flooding, mostly around the capital of Monrovia. Flooding in this region often destroys homes leaving many displaced. In addition, the healthcare system is highly dependent on foreign aid and assistance with Agencies and NGOs providing 90% of health services. Attribute Table Field DescriptionsISO3 - International Organization for Standardization 3-digit country code ADM0_NAME - Administration level zero identification / name AREA_AFF - Location of disaster event TYPE_1 - Primary classification in geodatabase TYPE_2 - Secondary classification in geodatabase START_DT - Start date of disaster event END_DT - Date disaster event ended TL_AFFECT - Total number affected by disaster event FATALITY - Number of deaths caused by disaster event COMMENTS - Additional comments SOURCE_DT - Source one creation date SOURCE - Source one SOURCE2_DT - Source two creation date SOURCE2 - Source two CollectionThis HGIS dataset was created through information provided by EM-DAT The International Disaster Database and Relief Web. Additional information regarding significant events was sourced through other sources and news articles. The data included herein have not been derived from a registered survey and should be considered approximate unless otherwise defined. While rigorous steps have been taken to ensure the quality of each dataset, DigitalGlobe is not responsible for the accuracy and completeness of data compiled from outside sources.Sources (HGIS)EM-DAT. The International Disaster Database, “Advanced Search”. Last modified September 2014. Accessed September 2014. http://www.emdat.be.ReliefWeb, “Liberia”. Last modified May 2014. Accessed September 2014. http://reliefweb.int.Sources (Metadata)IRIN Humanitarian News and Analysis, “Liberia: Floods displace hundreds in Monrovia.” Last modified August 2007. Accessed September 2014. http://www.irinnews.org.World Health Organization, “Liberia: Health Sector Needs Assessment.” Last modified January 2006. Accessed September 2014. http://www.who.int/en

The aim of the Social Protection Responses to Forced Displacement programme is to better understand effective mechanisms for linking social protection programmes and humanitarian assistance. By providing clearer guidance about when, how and why different linkages might be considered, the project will develop the theory, evidence base and operational guidance on how social protection systems and humanitarian systems can work together to meet the needs of those affected by displacement crises, including not only the displaced but vulnerable households in their host communities as well. The research is grounded in three country contexts with a total of six study sites that present different contexts of displacement and humanitarian response: Greece (Athens and Ioannina) Colombia (Bogotá and Cúcuta) Cameroon (far north and east) The project is led by ODI in close collaboration with the Centre for Applied Social Sciences Research and Training (CASS-RT) in Cameroon, the School of Government at the University of Los Andes in Colombia and the National Centre for Social Research (EKKE) in Greece. This work is part of the programme ‘Building the Evidence on Protracted Forced Displacement: A Multi-Stakeholder Partnership’. It is funded by the United Kingdom’s Foreign, Commonwealth and Development Office (FCDO), is managed by the World Bank Group (WBG) and was established in partnership with the United Nations High Commissioner for Refugees (UNHCR). The programme aims to expand global knowledge on forced displacement by funding quality research and disseminating results for the use of practitioners and policy makers.