World Refugee Day! Recent GLO Refugee Research.

On June 20 is World Refugee Day declared by the United Nations to raise awareness COVID-19 has replaced the refugee topic from the top ranks of the World’s challenges, but it is still there. UNHCR has published last Thursday its Global Report 2019 on the world-wide refugee situation. UNHCR Figures at a Glance:

Figures at a glance

This was all before COVID-19. The pandemic has made the situation much more dramatic, in many ways. Researchers can contribute in the long-run through studies on the sources of conflict, on the way to successfully integrate refugees into host and new home countries, and helping to develop proper policy responses.

Featured photo by Ra Dragon on Unsplash

Recent studies by GLO Researchers:

GLO Discussion Paper No. 562 
Occupational Sorting and Wage Gaps of Refugees
Baum, Christopher F. & Lööf, Hans & Stephan, Andreas & Zimmermann, Klaus F.


Refugee workers start low and adjust slowly to the wages of comparable natives. The innovative approach in this study using unique Swedish employer-employee data shows that the observed wage gap between established refugees and comparable natives is mainly caused by occupational sorting into cognitive and manual tasks. Within occupations, it can be largely explained by differences in work experience. The identification strategy relies on a control group of matched natives with the same characteristics as the refugees,using panel data for 2003–2013 to capture unobserved heterogeneity.

GLO Discussion Paper No. 538 
Estimating Poverty among Refugee Populations: A Cross-Survey Imputation Exercise for Chad – Download PDF
Beltramo, Theresa & Dang, Hai-Anh H. & Sarr, Ibrahima & Verme, Paolo


Household consumption surveys do not typically cover refugee populations, and poverty estimates for refugees are rare. This paper tests the performance of cross-survey imputation methods to estimate poverty for a sample of refugees in Chad, by combining United Nations High Commissioner for Refugees survey and administrative data. The proposed method offers poverty estimates based on administrative data that fall within a 95 percent margin of poverty estimates based on survey consumption data. This result is robust to different poverty lines, sets of regressors, and modeling assumptions of the error term. The method outperforms common targeting methods, such as proxy means tests and the targeting method currently used by humanitarian organizations in Chad.