Hi all,

I'm currently writing my thesis in Finance on the explanatory power of ESG scores on the delisting decision of firms. For this study I have collected deal data with ISIN codes of target firms and collected E-S- and G pillar scores, Tobin's Q, Log Assets (Size), Firm Age and industry classifications per firm. I have now created two samples: one with delisted firms (around 400 firms) and one with listed firms (around 800 firms). My dependent variable, delisting, is a binary response variable.

My hypothesis is that smaller firms with low ESG scores have a higher probability of delisting from the exchange. My supervisor told me that because of the way I have collected my sample, I need to use propensity score matching first to match a delisted firm with a listed firm of similar size/industry. However, I am not sure which test I need to use after the propensity score matching. Another professor has mentioned the Heckman command in STATA, which could solve the problem with selection bias that might be present in the current model. In my Econometrics textbook there is an example of a probit model trying to explain whether a firm is taken over by another firm during a given year, and uses a similar formula as my model. However, I am unsure about the sample being used in the textbook.

I am not getting clear answers from supervisors and therefore I have turned to this platform to ask people with a little bit more expertise about which test suits this study best and how to implement this in STATA.

Hope to hear from you soon.

Kind regards,

V. Maurits