Logistic regression problem: propensity score matching

I am doing one part of an evaluation of a mandatory welfare-to-work 
programme in the UK.
As with all evaluations, the problem is to determine what would have 
happened if the initiative had not taken place.
In our case, we have a number of pilot areas and no possibility of 
random assignment.
Therefore we have been given control areas.
My problem is to select for survey individuals in the control areas who 
match as closely as possible the randomly selected sample of action area 
participants.
As I understand the methodology, the procedure is to run a logistic 
regression to determine the odds of a case being in the sample, across 
both action and control areas, and then choose for control sample the 
control area individual whose odds of being in the sample are closest to 
an actual sample member.

So far, I have following the multinomial logistic regression example in 
Fox's Companion to Applied Regression.
Firstly, I would like to know if the predict() is producing odds ratios 
(or probabilities) for being in the sample, which is what I am aiming 
for. 

Secondly, how do I get rownames (my unique identifier) into the 
output from predict() - my input may be faulty somehow and the wrong 
rownames being picked up - as I need to export back to database to sort 
and match in names, addresses and phone numbers for my selected samples.

My code is as follows:
londonpsm <- sqlFetch(channel, "London_NW_london_pilots_elig", 
rownames=ORCID)
attach(londonpsm)
mod.multinom <- multinom(sample ~ AGE + DISABLED + GENDER + ETHCODE + 
NDYPTOT + NDLTUTOT + LOPTYPE)
lonoutput <- predict(mod.multinom, sample, type='probs')
london2 <- data.frame(lonoutput)

The Logistic regression seems to work, although summary() says the it is 
not a matrix.

The output looks like odds ratios, but I would like to know whether this 
is so.