[Ijeoma Ugwuanyi]

Well, if you were going to pursue this just from a (generalized) DID perspective, you would create a variable that is 1 during the post-death period for those firms that experienced one, and 0 in all other observations (including both pre-death and all years for firms not-experiencing a death.) Let's call that variable experienced. Then you would set up a model like:

xtset firmid
xtreg outcome i.experienced i.year, fe

You have, however, properly recognized that this approach might fare poorly, and might fail the parallel trends test. So you're thinking about propensity score matching. I don't think I would go that route, however. First, propensity score matching is difficult in longitudinal data because the variables you match on sometimes change from one year to the next, and you often end up matching the same firm with different control firms in different years, which makes the analysis a bit confusing to interpret. Also, I think there are better ways to use propensity scores than through matching, such as using them as weights or even just including them as covariates. Suffice it to say that I am just not a big fan of propensity score matching--other reasonable people may disagree.

There are a few traits of a firm that I think are particularly important to get an exact match on here. The first is the number of members on the board of directors. A firm that has, say, 20 directors evidently has something like twice the chance of experiencing a director death as one that has only 10 directors. And size of board of directors is probably also related to some financial outcomes, if only because, for example, larger businesses (# of empoyees, revenues) will tend to have larger boards, I would think. The other thing that is relevant is the age of the directors. Older ones are more likely to die. And the age of the board members is probably different according to sector, business size and other attributes relevant to financial outcome. At least that's how it looks to me: remember, I'm an epidemiologist, I know next to nothing about finance.

Anyway, I'd be more inclined to get exact match on number of board members, and at least a reasonably close match on average director age. Then if you still have enough control firms (i.e. those with no director death) to go around and you want to further match on another variable or two, go ahead. Everything else that's relevant can just be a covariate in the model.

Lacking expertise in this area, I can't say anything more specific than that.

Thank you very much Clyde for your reply and for your suggestions nevertheless.

It is a very wise approach you have suggested to look for close matches using some key variables. Indeed the propensity score matching is almost impossible automatically, as there are no pre and post periods for the control firms. Everything is 0 for both pre and post for control groups (those without death experience). I have tried this to know.

I also note the issue about the changing nature of the variables matched on year after year using a propensity score on a longitudinal data.

I will work around your suggestions on how to proceed from here.


Kind regards,
Ijeoma