One of the most interesting talks that I've heard so far, here at the Linguistic Society of America's annual meeting, was Uri Tadmor and Martin Haspelmath, "Measuring the borrowability of word meanings". I haven't yet been able to get a copy of the slides for their presentation here, but web search turned up the abstract for a talk of the same title at the upcoming Swadesh Centenary Conference, and the slides from a talk entitled "Loanword Typology: Investigating lexical borrowability in the world's languages", given at a recent workshop "New Directions in Historical Linguistics"(Université de Lyons, May 12-14 2008).
[Update: the slides from their LSA talk are now here, and additional information is available on the project website. I'll update the rest of this post to match when I have a chance. Meanwhile, Uri emphasizes that the LSA results are preliminary, and the Lyons report even more so.]
[Update #2: Uri answers questions in a guest post here.]
As you can learn from those links, their project investigated the words for 1460 "meanings" in 30 languages, allowing for a many-to-many relationship between words and meanings. They recruited an expert for each language to find the relevant words and to determine various properties for each one, including whether it had been borrowed from another language. The resulting database will be posted on the web at some point in the not-too-distant future.
(Since I haven't yet been able to get a copy of yesterday's slides, the numbers and lists below come from their May presentation, and so are somewhat out of date, since as I understand it, the construction and checking of the database is not quite complete even now.)
The most loanword-friendly languages (in their set of 30) were Selice Romani (60%), Tarifiyt Berber (48%), Romanian (40%), English (39%), and Sramaccan (34%). The most loanword-resistant languages were Mandarin Chinese (1%), Ket (7%), Manage (7%), Seychelles Creole (8%), and Gurindji (9%).
The most borrowable word meanings were kangaroo (100%), olive (100%), motor (96%), camel (95%), coffee (93%).
Here's their slide for the other end of the scale (click for a larger version):
There are several reasons for being interested in this sort of thing, but one of the most important ones is the reason that motivated Morris Swadesh to compile his list, half a century ago.