Department Brown Bag Seminars

Fall 2017

Wednesday 12:001:05 p.m.
499 Engineering II

September 27
Soo Hong Chew, National U of Singapore
"Ellsberg Meets Keynes: Missing Links Among Attitudes Toward Sources of Uncertainty"
Engineering 2, room 280


October 11
Theresa Beltramo
“Reaching Poor Refugees: Targeting Food and Multi-Sectoral Cash Transfers in Niger”
Abstract
The demand for humanitarian financing globally has steadily increased from 2012 (16.1 billion) to 2016 (27.3 billion) in pace with the growing number of forcibly displaced people globally- 65.6 million forcibly displace, 17.2 million refugees under UNHCR’s mandate. UNHCR’s funding levels and gaps resemble those at the global level for humanitarian actors, between 2010 and 2016, as contributions to UNHCR have more than doubled, increasing from US $1.8 billion to US $3.9 billion over the same time period.  While UNHCR’s financing has reached historic highs, the needs continue to grow at an even faster pace. As of July 31, 2017 the total amount pledged to UNHCR was 38% of the total global appeal or 2.55 billion pledged of 7.91 billion assessed as needed. In light of this financing gap, UNHCR and other humanitarian partners are forced to prioritize among the needs assessed. Due to this funding gap, basic assistance particularly for protracted forcibly displaced situations like the Malian refugees in Niger, has to be prioritized. Traditionally humanitarian agencies and partners have targeted assistance using categorical targeting, or criteria based on certain vulnerabilities or needs of individuals or household. In Niger, UNHCR and WFP faced the resource challenge of targeting limited food assistance and forthcoming multi-purpose cash. To identify the best tool for targeting assistance to Malian refugees in Niger, we estimate household welfare by producing a household capacity score for each of the methods implemented (Proxy Means Testing, Household Economy Approach, and Principal Components Analysis). All models show that the predictive power of the HEA models are weaker than the PMT model. The levels of R2 range between 10 to 26 percent, corresponding to approximately half of the levels reached in the PMT models. The PCA model confirms the relationship between explanatory variables for monetary poverty but PMT performs better than RCA as a larger share of variance is explained by the model.  We find for the Intikane site, the pre-existing categorical targeting used to target food assistance prior to this exercise is performing poorly as those who receive the equivalent of half of a daily food ration are poorer than those who receive 100%. 

 


October 18
Asha Shepard
"TBA"


October 25
Eilin Francis
"TBA"


November 1
Dan Friedman
"TBA"


November 8
Jon Robinson
"TBA"


November 15
Jeremy West
"TBA"


November 22
Grace Gu
"Unemployment During Sovereign Crises"


November 29
Dario Pozzoli
"The Impact of Immigration on Firm-Level Offshoring"


December 6
Justin Marion
"TBA"