News Finding: In other words, the locations and the number of refugees remain the same, they are just matched more precisely and systematically.
Paper Finding: This indicates that there are synergies between places and people; certain characteristics will make a refugee a better match for a particular location.
News Context: Canton Actual With algorithm OW 0% 40% TI 4% 32% FR 4% 15% LU 7% 18% VS 7% 14% VD 8% 8% BL 11% 12% AG 11% 21% GE 13% 23% ZH 15% 7% BE 16% 35% JU 17% 21% SG 17% 28% SH 20% 51% UR 20% 48% ZG 20% 61% GL 22% 42% BS 25% 25% NE 25% 11% SO 25% 64% GR 29% 64% NW 29% 45% SZ 29% 18% TG 30% 33% AR 33% 46%
  
  Here's roughly how the algorithm works: The process starts with a series of models trained to predict where a certain type of refugee is likely to fare best based on previous cases. In other words, the locations and the number of refugees remain the same, they are just matched more precisely and systematically. "It's really hard for a human to juggle all of those."
Paper Context: Finally, the expected employment returns associated with personal characteristics can vary across different resettlement locations (Fig. This indicates that there are synergies between places and people; certain characteristics will make a refugee a better match for a particular location. In Switzerland, for example, we find that the ability to speak French (i.e., among French-speaking African refugees) results in a larger payoff for refugees assigned to Frenchspeaking cantons than for those assigned to German-speaking cantons ( fig.